Patent Publication Number: US-7912711-B2

Title: Method and apparatus for speech data

Description:
This is a continuation of application Ser. No. 10/089,925, filed Aug. 9, 2002 now U.S. Pat. No. 7,283,961 pursuant to 35 USC 371 and based on International Application PCT/JP01/06708 filed Aug. 3, 2001, entitled to the priority filing dates of Japanese applications 2000-241062, 2000-251969, 2000-346675 filed in Japan on Aug. 9, Aug. 23 and Nov. 14, 2000, respectively, the entirety of which are incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     This invention relates to a method and an apparatus for processing data, a method and an apparatus for learning and a recording medium. More particularly, it relates to a method and an apparatus for processing data, a method and an apparatus for learning and a recording medium according to which the speech coded in accordance with the CELP (code excited linear prediction coding) system can be decoded to the speech of high sound quality. 
     BACKGROUND ART 
     First, an instance of a conventional portable telephone set is explained with reference to  FIGS. 1 and 2 . 
     This portable telephone set is adapted for performing transmission processing of coding the speech into a preset code in accordance with the CELP system and transmitting the resulting code, and for performing the receipt processing of receiving the code transmitted from other portable telephone sets and decoding the received code into speech.  FIGS. 1 and 2  show a transmitter for performing transmission processing and a receiver for performing receipt processing, respectively. 
     In the transmitter, shown in  FIG. 1 , the speech uttered by a user is input to a microphone  1  where the speech is transformed into speech signals as electrical signals, which are routed to an A/D (analog/digital) converter  2 . The A/D converter  2  samples the analog speech signals from the microphone  1  with, for example, the sampling frequency of 8 kHz, for A/D conversion to digital speech signals, and further quantizes the resulting digital signals with a preset number of bits to route the resulting quantized signals to an operating unit  3  and to an LPC (linear prediction coding) unit  4 . 
     The LPC unit  4  performs LPC analysis of speech signals from the A/D converter  2 , in terms of a frame corresponding to e.g., 160 samples as a unit, to find p-dimensional linear prediction coefficients α 1 , α 2 , . . . , α P . The LPC analysis unit  4  sends a vector, having these P-dimensional linear prediction coefficients α P , where P=1, 2, . . . , P, as components, to a vector quantizer  5 , as a feature vector α of the speech. 
     The vector quantizer  5  holds a codebook, associating the code vector, having the linear prediction coefficients as components, with the code, and quantizes the feature vector α from the LPC analysis unit  4 , based on this codebook, to send the code resulting from the vector quantization, sometimes referred to below as A code (A_code), to a code decision unit  15 . 
     The vector quantizer  5  sends the linear prediction coefficients α 1 , α 2 , . . . , α P ′, as components forming the code vector α′ corresponding to the A code, to a speech synthesis filter  6 . 
     The speech synthesis filter  6  is e.g., a digital filter of the IIR (infinite impulse response) type, and executes speech synthesis, with the linear prediction coefficients α P ′, where p=1, 2, . . . , P, from the vector quantizer  5  as tap coefficients of the IIR filter and with the residual signals e from an operating unit  14  as an input signal. 
     That is, in the LPC analysis, executed by the LPC unit  4 , it is assumed that a one-dimensional linear combination represented by the equation (1):
 
 s   n +α 1   s   n−1 +α 2   s   n−2 + . . . +α p   s   n−p   =e   n   (1)
 
holds, where s n  is the (sampled value of) the speech signal at the current time n and s n−1 , s n−2 , . . . , s n−p  are past P sample values neighboring thereto, and the linear prediction coefficients α p , which will minimize the square error between the actual sample value s n  and a value of linear prediction s n ′ thereof in case the predicted value (linear prediction value) s n ′ of the sampled value of the speech signal s n  at the current time is linear-predicted from the n past sample values s n−1 , s n−2 , . . . , s n−P  in accordance with the following equation (2):
 
 s   n ′=−(α 1   s   n−1 +α 2   s   n−2 + . . . +α p   s   n−p )  (2)
 
is found.
 
     In the above equation (1), {e n } ( . . . , e n−1 , e n , e n+1 , . . . ) are reciprocally non-correlated probability variables with an average value equal to 0 and with a variance equal to a preset value of β 2 . 
     From the equation (1), the sample value s n  may be represented by the following equation (3):
 
 s   n   =e   n −(α 1   s   n−1 +α 2   s   n−2 + . . . +α p   s   n−p )  (3)
 
This may be Z-transformed to give the following equation (4):
 
 S=E /(1+α 1   z   −1 +α 2   z   −2 + . . . +α p   z   −P )  (4)
 
where S and E denote Z-transforms of s n  and e n  in the equation (3), respectively.
 
     From the equations (1) and (2), e n  can be represented by the following equation (5):
 
 e   n   =s   n   −s   n ′  (5)
 
and is termed a residual signal between the real sample value s n  and linear predicted value s n ′ thereof.
 
     Thus, the speech signal s n  may be found from the equation (4), using the linear prediction coefficients α P  as tap coefficients of the IIR filter and also using the residual signal e n  as an input signal to the IIR filter. 
     The speech synthesis filter  6  calculates the equation (4), using the linear prediction coefficients α p ′ from the vector quantizer  5  as tap coefficients and also using the residual signal e from the operating unit  14  as an input signal, as described above, to find speech signals (synthesized speech signals) ss. 
     Meanwhile, since the speech synthesis filter  6  uses not the linear prediction coefficients α p , obtained as the result of the LPC by the LPC unit  4 , but the linear prediction coefficients α p ′ as a code vector corresponding to the code obtained by its vector quantization. So, the synthesized speech signal output by the speech synthesis filter  6  is not the same as the speech signal output by the A/D converter  2 . 
     The synthesized sound signal ss, output by the speech synthesis filter  6 , is sent to the operating unit  3 , which subtracts the speech signal s, output from the A/D converter  2 , from the synthesized speech signal ss from the speech synthesis filter  6 , to send the resulting difference value to a square error operating unit  7 . The square error operating unit  7  finds the square sum of the difference values from the operating unit  3  (square sum of the sample values of the k&#39;th frame) to send the resulting square sum to a minimum square sum decision unit  8 . 
     The minimum square sum decision unit  8  holds an L-code (L_code) as a code representing the lag, a G-code (G_code) as a code representing the gain and an I-code (I_code) as the code representing the codeword, in association with the square error output by the square error operating unit  7 , and outputs the I-code, G-code and the L-code corresponding to the square error output from the square error operating unit  7 . The L-code, G-code and the I-code are sent to an adaptive codebook storage unit  9 , a gain decoder  10  and to an excitation codebook storage unit  11 , respectively. The L-code, G-code and the I-code are also sent to a code decision unit  15 . 
     The adaptive codebook storage unit  9  holds an adaptive codebook, which associates e.g., a 7-bit L-code with a preset delay time (lag), and delays the residual signal e supplied from the operating unit  14  by a delay time associated with the L-code supplied from the minimum square error decision unit  8  to output the resulting delayed signal to an operating unit  12 . 
     Since the adaptive codebook storage unit  9  outputs the residual signal e with a delay corresponding to the L-code, the output signal may be said to be a signal close to a periodic signal having the delay time as a period. This signal mainly becomes a driving signal for generating a synthesized sound of the voiced sound in the speech synthesis employing linear prediction coefficients. 
     The gain decoder  10  holds a table which associates the G-code with the preset gains β and γ, and outputs gain values β and γ associated with the G-code supplied from the minimum square error decision unit  8 . The gain values β and γ are supplied to the operating units  12  and  13 . 
     An excitation codebook storage unit  11  holds an excitation codebook, which associates e.g., a 9-bit I-code with a preset excitation signal, and outputs the excitation signal, associated with the I-code output from the minimum square error decision unit  8 , to the operating unit  13 . 
     The excitation signal stored in the excitation codebook is a signal close e.g., to the white noise and becomes a driving signal mainly used for generating the synthesized sound of the unvoiced sound in the speech synthesis employing linear prediction coefficients. 
     The operating unit  12  multiplies an output signal of the adaptive codebook storage unit  9  with the gain value β output by the gain decoder  10  and routes a product value  1  to the operating unit  14 . The operating unit  13  multiplies the output signal of the excitation codebook storage unit  11  with the gain value γ output by the gain decoder  10  to send the resulting product n to the operating unit  14 . The operating unit  14  sums the product value  1  from the operating unit  12  with the product value n from the operating unit  13  to send the resulting sum as the residual signal e to the speech synthesis filter  6 . 
     In the speech synthesis filter  6 , the input signal, which is the residual signal e, supplied from the operating unit  14 , is filtered by the IIR filter, having the linear prediction coefficients α p ′ supplied from the vector quantizer  5  as tap coefficients, and the resulting synthesized signal is sent to the operating unit  3 . In the operating unit  3  and the square error operating unit  7 , operations similar to those described above are carried out and the resulting square errors are sent to the minimum square error decision unit  8 . 
     The minimum square error decision unit  8  verifies whether or not the square error from the square error operating unit  7  has becomes smallest (locally minimum). If it is verified that the square error is not locally minimum, the minimum square error decision unit  8  outputs the L code, G code and the I code, corresponding to the square error, and subsequently repeats a similar sequence of operations. 
     If it is found that the square error has become smallest, the minimum square error decision unit  8  outputs a definite signal to the code decision unit  15 . The code decision unit  15  is adapted for latching the A code, supplied from the vector quantizer  5 , and for sequentially latching the L code, G code and the I code, sent from the minimum square error decision unit  8 . On receipt of the definite signal from the minimum square error decision unit  8 , the code decision unit  15  sends the A code, L code, G code and the I code, then latched, to a channel encoder  16 . The channel encoder  16  then multiplexes the A code, L code, G code and the I code, sent from the code decision unit  15 , to output the resulting multiplexed data as code data, which code data is transmitted over a transmission channel. 
     For simplicity in explanation, the A code, L code, G code and the I code are assumed to be found from frame to frame. It is however possible to divide e.g., one frame into four sub-frames and to find the L code, G code and the I code on the sub-frame basis. 
     It should be noted that, in  FIG. 1 , as in  FIGS. 2 ,  11  and  12 , explained later on, an array variable [k] is formed by affixing [k] to each variable. In the present specification, explanation on this k, representing the number of frames, is sometimes omitted. 
     The code data, sent from a transmitter of another portable telephone set, is received by a channel decoder  21  of a receiver shown in  FIG. 2 . The channel decoder  21  decodes the L code, G code, I code and the A code from the cod data to send the so separated respective codes to an adaptive codebook storage unit  22 , a gain decoder  23 , an excitation codebook storage unit  24  and to a filter coefficient decoder  25 . 
     The adaptive codebook storage unit  22 , gain decoder  23 , excitation codebook storage unit  24  and the operating units  26  to  28  are configured similarly to the adaptive codebook storage unit  9 , gain decoder  10 , excitation codebook storage unit  11  and the operating units  12  to  14 , respectively, and perform the processing similar to that explained with reference to  FIG. 1  to decode the L code, G code and the I code into the residual signal e. This residual signal e is sent as an input signal to a speech synthesis filter  29 . 
     A filter coefficient decoder  25  holds the same codebook as that stored in the vector quantizer  5  of  FIG. 1  and decodes the A code to the linear prediction coefficient α p ′ which is then routed to the speech synthesis filter  29 . 
     The speech synthesis filter  29  is configured similarly to the speech synthesis filter  6  of  FIG. 1 , and solves the equation (4), with the linear prediction coefficient α p ′ from the filter coefficient decoder  25  as a tap coefficient and with the residual signal e from the operating unit  28  as an input signal, to generate a synthesized speech signal when the square error has been found to be minimum by the minimum square error decision unit  8  of  FIG. 1 . This synthesized speech signal is sent to a D/A (digital/analog) converter  30 . The D/A converter  30  D/A converts the synthesized speech signal from the speech synthesis filter  29  to send the resulting analog signal to a loudspeaker  31  as output. 
     The transmitter of the portable telephone set transmits an encoded version of the residual signal and the linear prediction coefficients, as filter data supplied to the speech synthesis filter  29  of the receiver, as described above. Thus, the receiver decodes the codes into the residual signal and the linear prediction coefficients. The so decoded residual signal and linear prediction coefficients are corrupted with errors, such as quantization errors. Thus, the so decoded residual signals and so decoded linear prediction coefficients, sometimes referred to below as decoded residual signals and decoded linear prediction coefficients, respectively, are not the same as the residual signal and linear prediction coefficients obtained on LPC analysis of the speech, so that the synthesized speech signals, output by the receiver&#39;s speech synthesis filter  29 , are distorted and therefore are deteriorated in sound quality. 
     DISCLOSURE OF THE INVENTION 
     In view of the above-described status of the art, it is an object of the present invention to provide a method and an apparatus for processing data, a method and an apparatus for learning and a recording medium, whereby th synthesized sound of high sound quality may be achieved. 
     For accomplishing the above object, the present invention provides a speech processing device including a class tap extraction unit for extracting class taps, used for classifying the target speech to one of a plurality of classes, from the code, a classification unit for finding the class of the target speech based on the class taps, an acquisition unit for acquiring the tap coefficients associated with the class of the target speech from among the tap coefficients as found on learning from class to class, and a prediction unit for finding the prediction values of the target speech using the prediction taps and the tap coefficients associated with the class of the target speech. With the speech of high sound quality, the prediction values of which are to be found, as the target speech, the prediction taps used for predicting the target speech are extracted from the synthesized sound. The class taps, used for sorting the target speech into one of plural classes, are extracted from the code, and the tap coefficients, associated with the class of the target speech, are acquired from the tap class-based coefficients as found on learning. The prediction values of the target speech are found using the prediction taps and the tap coefficients associated with the class of the target speech. 
     The learning device according to the present invention includes a class tap extraction unit for extracting class taps from the code, the class taps being used for classifying the speech of high sound quality, as target speech, the prediction values of which are to be found, a classification unit for finding a class of the target speech based on the class taps, and a learning unit for carrying out learning so that the prediction errors of the prediction values of the speech of high sound quality obtained on carrying out predictive calculations using the tap coefficients and the synthesized sound will be statistically minimum, to find the tap coefficients from class to class. With the speech of high sound quality, the prediction values of which are to be found, as the target speech, the class taps used for sorting the target speech to one of plural classes are extracted from the code, and the class of the target speech is found based on the class taps, by way of classification. The learning then is carried out so that the prediction errors of the prediction values of the speech of high sound quality, as obtained in carrying out predictive calculations using the tap coefficients and the synthesized sound, will be statistically smallest to find the class-based tap coefficients. 
     The data processing device according to the present invention includes a code decoding unit for decoding the code to output decoded filter data, an acquisition unit for acquiring preset tap coefficients as found by carrying out learning, and a prediction unit for carrying out preset predictive calculations, using the tap coefficients and the decoded filter data, to find prediction values of the filter data, to send the so found prediction values to the speech synthesis filter. The code is decoded, and the decoded filter data is output. The preset tap coefficients, as found on effecting the learning, are acquired, and preset predictive calculations are carried out using the tap coefficients and the decoded filter data to find predicted values of the filter data, which then is output to the speech synthesis filter. 
     The learning device according to the present invention includes a code decoding unit for decoding the code corresponding to filter data to output decoded filter data, and a learning unit for carrying out learning so that the prediction errors of prediction values of the filter data obtained on carrying out predictive calculations using the tap coefficients and decoded filter data will be statistically smallest to find the tap coefficients. The code associated with the filter data is decoded and the decoded filter data is output in a code decoding step. Then, learning is carried out so that prediction errors of the prediction values of the filter data obtained on carrying out predictive calculations using the tap coefficients and the decoded filter data will be statistically minimum. 
     The speech processing device according to the present invention includes a prediction tap extraction unit for extracting prediction taps usable for predicting the speech of high sound quality, as target speech, the prediction values of which are to be found, a class tap extraction unit for extracting class taps, usable for sorting the target speech to one of a plurality of classes, by way of classification, from the synthesized sound, the code or the information derived from the code, an acquisition unit for acquiring the tap coefficients associated with the class of the target speech from the tap coefficients as found on learning from one class to another, and a prediction unit for finding the prediction values of the target speech using the prediction taps and the tap coefficients associated with the class of the target speech. With the speech of high sound quality, the prediction values of which are to be found, as the target speech, the prediction taps, used for predicting the target speech, are extracted from the synthesized sound and the code or the information derived from the code, and the class taps, used for sorting the target speech to one of plural classes, are extracted from the synthesized sound, code or the information derived from the code. Based on the class taps, classification is carried out for finding the class of the target speech. From the class-based tap coefficients, as found on learning, the tap coefficient associated with the class of the target speech are acquired. The prediction values of the target speech are found using the prediction taps and the tap coefficients associated with the class of the target speech. 
     The learning device according to the present invention includes a prediction tap extraction unit for extracting prediction taps usable in predicting the speech of high sound quality, as target speech, the prediction values of which are to be found, from the synthesized sound, the code or from the information derived from the code, a class tap extraction unit for extracting class taps usable for sorting the target speech to one of a plurality of classes, by way of classification, from the synthesized sound, the code or from the information derived from the code, a classification unit for finding the class of the target speech based on the class taps, and a learning unit for carrying out learning so that the prediction errors of prediction values of the speech of high sound quality, obtained on carrying out predictive calculations using the tap coefficients and the prediction taps, will be statistically smallest. With the speech of the high sound quality, the prediction values of which are to be found, as the target speech, the prediction taps, used for predicting the target speech, are extracted from the synthesized sound and the code or from the information derived from the code. The class of the target speech is found, based on the class taps, by way of classification. Then, learning is carried out so that the prediction errors of the prediction values of the target speech acquired on carrying out the predictive calculations using the tap coefficients and the prediction taps will be statistically smallest to find the tap coefficients on the class basis. 
     Other objects, features and advantages of the present invention will become more apparent from reading the embodiments of the present invention as shown in the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram showing a typical transmitter forming a conventional portable telephone receiver. 
         FIG. 2  is a block diagram showing a typical receiver. 
         FIG. 3  is a block diagram showing a speech synthesis device embodying the present invention. 
         FIG. 4  is a block diagram showing a speech synthesis filter forming the speech synthesis device. 
         FIG. 5  is a flowchart for illustrating the processing of a speech synthesis device shown in  FIG. 3 . 
         FIG. 6  is a block diagram showing a learning device embodying the present invention 
         FIG. 7  is a block diagram showing a prediction filter forming the learning device according to the present invention. 
         FIG. 8  is a flowchart for illustrating the processing by the learning device of  FIG. 6 . 
         FIG. 9  is a block diagram showing a transmission system embodying the present invention. 
         FIG. 10  is a block diagram showing a portable telephone set embodying the present invention. 
         FIG. 11  is a block diagram showing a receiver forming the portable telephone set. 
         FIG. 12  is a block diagram showing a modification of the learning device embodying the present invention. 
         FIG. 13  is a block diagram showing a typical structure of a computer embodying the present invention. 
         FIG. 14  is a block diagram showing another typical structure of a speech synthesis device embodying the present invention. 
         FIG. 15  is a block diagram showing a speech synthesis filter forming the speech synthesis device. 
         FIG. 16  is a flowchart for illustrating the processing of the speech synthesis device shown in  FIG. 14 . 
         FIG. 17  is a block diagram showing another modification of the learning device embodying the present invention. 
         FIG. 18  is a block diagram showing a prediction filter forming the learning device according to the present invention. 
         FIG. 19  is a flowchart for illustrating the processing of the learning device shown in  FIG. 17 . 
         FIG. 20  is a block diagram showing a transmission system embodying the present invention. 
         FIG. 21  is a block diagram for illustrating the portable telephone set embodying the present invention. 
         FIG. 22  is a block diagram showing the receiver forming the portable telephone set. 
         FIG. 23  is a block diagram showing still another modification of the learning device embodying the present invention. 
         FIG. 24  is a block diagram showing still another typical structure of a speech synthesis device embodying the present invention. 
         FIG. 25  is a block diagram showing a speech synthesis filter forming the speech synthesis device. 
         FIG. 26  is a flowchart for illustrating the processing of the speech synthesis device shown in  FIG. 24 . 
         FIG. 27  is a block diagram showing a further modification of the learning device embodying the present invention. 
         FIG. 28  is a block diagram showing a prediction filter forming the learning device according to the present invention. 
         FIG. 29  is a flowchart for illustrating the processing of the learning device shown in  FIG. 27 . 
         FIG. 30  is a block diagram showing a transmission system embodying the present invention. 
         FIG. 31  is a block diagram showing a portable telephone set embodying the present invention. 
         FIG. 32  is a block diagram showing a receiver forming the portable telephone set. 
         FIG. 33  is a block diagram showing a further modification of the learning device embodying the present invention. 
         FIG. 34  shows teacher and pupil data. 
     
    
    
     BEST MODE FOR CARRYING OUT THE INVENTION 
     Referring to the drawings, certain preferred embodiments of the present invention will be explained in detail. 
     The speech synthesis device, embodying the present invention, is configured as shown in  FIG. 3 , and is fed with code data obtained on multiplexing the residual code and the A code obtained in turn respectively on coding residual signals and linear prediction coefficients, to be supplied to a speech synthesis filter  44 , by vector quantization. From the residual code and the A code, the residual signals and linear prediction coefficients are decoded, respectively, and fed to the speech synthesis filter  44 , to generate the synthesized sound. The speech synthesis device executes predictive calculations, using the synthesized sound produced by the speech synthesis filter  44  and also using tap coefficients as found on learning, to find the high quality synthesized speech, that is the synthesized sound with improved sound quality. 
     With the speech synthesis device of the present invention, shown in  FIG. 3 , classification adaptive processing is used to decode the synthesized speech to high quality true speech, more precisely predicted values thereof. 
     The classification adaptive processing is comprised of classification and adaptive and processing. By the classification, the data is classified depending on its characteristics and subjected to class-based adaptive processing. The adaptive processing uses the following technique: 
     That is, the adaptive processing finds predicted values of the true speech of high sound quality by, for example, the linear combination of the synthesized speech and preset tap coefficients. 
     Specifically, it is now contemplated to find predicted values E[y] of the high quality speech as teacher data, using, as teacher data, the speech of the true speech of high quality, more precisely the samples values thereof, and also using, as pupil data, the synthesized speech obtained on coding the true speech of high quality into the L code, G code, I code and the A code, in accordance with the CELP system, and subsequently on decoding these codes by the receiver shown in  FIG. 2 , by a model of one-dimensional linear combination defined by a set of synthesized sounds, more precisely sample values thereof, that is x 1 , x 2 , . . . , and a linear combination of preset tap coefficients w 1 , w 2 , . . . . It is noted that the prediction value E[y] may be represented by the following equation:
 
 E[y]=w   1   x   1   +w   2   x   2 +. . .   (6).
 
     If, for generalizing the equation (6), a matrix W formed by a set of tap coefficients w j , a matrix X formed by a set of pupil data x ij  and a matrix Y′ formed by a set of prediction values E[y i ] are defined as: 
             X   =     [           x   11           x   12         ⋯         x     i   ⁢           ⁢   J                 x   21           x   22         ⋯         x     2   ⁢           ⁢   J               ⋯       ⋯       ⋯       ⋯             x     I   ⁢           ⁢   1             x     I   ⁢           ⁢   2           ⋯         x   IJ           ]                 W   =         [           w   1               w   2             ⋯             w   J           ]     ⁢     Y       ′           ′       =     [           E   ⁡     [     y   1     ]                 E   ⁡     [     y   2     ]               ⋯             E   ⁡     [     y   1     ]             ]             
the following observation equation:
 
XW=Y′  (7)
 
holds.
 
     It is noted that the component x ij  of the matrix X denotes the column number j of pupil data in the set of the number i row of pupil data (set of pupil data used in predicting teacher data y i  of the number i row of teacher data) and that the component w j  of the matrix W denotes the tap coefficient a product of which with the number j column of pupil data in the set of pupil data is to be found. It is also noted that y i  denotes the number i row of teacher data and hence E[y i ] denotes the predicted value of the number i row of teacher data. It is also noted that a suffix i of the component y i  of the matrix Y is omitted from y on the left side of the equation (6) and that a suffix i is similarly omitted from the component x ij  of the matrix X. 
     It is now contemplated to apply the least square method to this observation equation to find a predicted value E[y] close to the true sound y of high quality. If the matrix Y formed by a set of speech y of high sound quality as teacher data and the matrix E formed by a set of residual signals e of the prediction values E[y] for the speech y of high sound quality are defined by: 
               E   =     [           e   1                     e   2             ⋯             e   T                 ]       ,     Y   =     [           y   1               y   2             ⋯             y   T           ]             
the following residual equation:
 
 XW=Y+E   (8)
 
holds from the equation (7).
 
     In this case, the tap coefficients w j  for finding the prediction value E[y] close to the true speech of high sound quality y may be found by minimizing the square error 
     
       
         
           
             
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     The tap coefficients for the case when the above square error, differentiated with the tap coefficient w j , is equal to zero, that is the tap coefficient w j  satisfying the following equation: 
                   e   1     ⁢       ∂     e   1         ∂     w   j           +       e   2     ⁢       ∂     e   2         ∂     w   j           +   ⋯   +       e   I     ⁢       ∂     e   I         ∂     w   J             =     0   ⁢     (       j   =   1     ,   2   ,     ⋯   ⁢           ⁢   J       )             
represents an optimum value for finding the predicted value E[y] close to the true speech y of high sound quality.
 
     First, the equation (8) is differentiated with respect to the tap coefficient w j  to obtain the following equation: 
     
       
         
           
             
               
                 
                   
                     
                       
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     From the equations (9) and (10), the following equation (11): 
                         ∑     i   =   1     I     ⁢       e   i     ⁢     x     i   ⁢           ⁢   1           =   0     ,         ∑     i   =   1     I     ⁢       e   i     ⁢     x     i   ⁢           ⁢   2           =   0     ,   ⋯   ⁢           ,         ∑     i   =   1     In     ⁢       e   i     ⁢     x   ij         =   0             (   11   )               
is obtained.
 
     Taking into account the relationships among pupil data x ij , tap coefficients w j , teacher data y i  and errors e i , in the residual equation (8), the following normal equations: 
                   {               (       ∑     i   =   1     I     ⁢       X   iJ     ⁢     X     i   ⁢           ⁢   1           )     ⁢     W   1       +       (       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   1       ⁢     X     i   ⁢           ⁢   2           )     ⁢     W   2       +   ⋯   +                   (       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   1       ⁢     X     i   ⁢           ⁢   J           )     ⁢     W   J       =     (       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   1       ⁢     Y   i         )                     (       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   2       ⁢     X     i   ⁢           ⁢   1           )     ⁢     W   1       +       (       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   2       ⁢     X     i   ⁢           ⁢   2           )     ⁢     W   2       +   ⋯   +                   (       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   2       ⁢     X     i   ⁢           ⁢   J           )     ⁢     W   J       =     (       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   2       ⁢     Y   i         )               …                 (       ∑     i   =   1     I     ⁢       X   iJ     ⁢     X     i   ⁢           ⁢   1           )     ⁢     W   1       +       (       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   J       ⁢     X     i   ⁢           ⁢   2           )     ⁢     W   2       +   ⋯   +                   (       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   J       ⁢     X     i   ⁢           ⁢   J           )     ⁢     W   J       =     (       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   J       ⁢     Y   i         )                     (   12   )               
is obtained.
 
     If the matrix (co-variance matrix) A and the vector v are defined by: 
             A   =     [             ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   1       ⁢     X     i   ⁢           ⁢   1       ⁢       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   1       ⁢     X     i   ⁢           ⁢   2       ⁢           ⁢   ⋯   ⁢           ⁢       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   1       ⁢     X     i   ⁢           ⁢   J                               ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   2       ⁢     X     i   ⁢           ⁢   1       ⁢       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   2       ⁢     X     i   ⁢           ⁢   2       ⁢           ⁢   ⋯   ⁢                 ∑       i   =   1     I     ⁢       X     i   ⁢           ⁢   2       ⁢     X     i   ⁢           ⁢   J                               ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   J       ⁢     X     i   ⁢           ⁢   1       ⁢       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   J       ⁢     X     i   ⁢           ⁢   2       ⁢           ⁢   ⋯   ⁢           ⁢       ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   J       ⁢     X     i   ⁢           ⁢   J                         ]                 v   =     (             ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   1       ⁢     Y   i                     ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   2       ⁢     Y   i                       ⋮               ∑     i   =   1     I     ⁢       X     i   ⁢           ⁢   J       ⁢     Y   i                     )           
and the vector W is defined as shown in the equation 1, the normal equation shown by the equation (12) may be expressed as:
 
AW=v  (13)
 
     A number the normal equations equal to the number J of the tap coefficients w j  to be found may be established as the normal equations of (12) by providing a certain number of sets of the pupil data x ij  and teacher data y i . Consequently, optimum tap coefficients, herein the tap coefficients that minimize the square error, may be found by solving the equation (13) with respect to the vector W. However, it is noted that, for solving the equation (13), the matrix A in the equation (13) needs to be regular, and that e.g., a sweep-out method (Gauss-Jordan&#39;s erasure method) may be used in the process for the solution. 
     It is the adaptive processing that finds the optimum tap coefficients w j  and uses the so found optimum tap coefficients w j  to find the prediction value E[y] close to the true speech of the high quality y using the equation (6). 
     If the speech signal sampled at a high sampling frequency, or speech signals employing a larger number of allocated bits, are used as teacher data, while the synthesized sound, obtained on decoding an encoded version by the CELP system of speech signals, obtained in turn on decimation or re-quantization employing a smaller number of bits of speech signals as the teacher data, is used as pupil data, such tap coefficients are used which will give the speech of high sound quality which statistically minimizes the prediction error in generating the speech signals sampled at a high sampling frequency, or speech signals employing a larger number of allocated bits. In this case, the synthesized speech of high sound quality may be produced. 
     In the speech synthesis device, shown in  FIG. 3 , code data, comprised of the A code and the residual code, may be decoded to the high sound quality speech by the above-described classification adaptive processing. 
     That is, a demultiplexer (DEMUX)  41 , supplied with code data, separates frame-based A code and the residual code from code data supplied thereto. The demultiplexer  41  routes the A code to a filter coefficient decoder  42  and to a tap generator  46 , while supplying the residual code to a residual codebook storage unit  43  and to a tap generator  46 . 
     It is noted that the A code and the residual code, contained in the code data in  FIG. 3 , are the codes obtained on vector quantization, with a preset codebook, of the linear prediction coefficients and the residual signals obtained on LPC speech analysis. 
     The filter coefficient decoder  42  decodes the frame-based A code, supplied thereto from the demultiplexer  41 , into linear prediction coefficients, based on the same codebook as that used in obtaining the A code, to supply the so decoded signals to a speech synthesis filter  44 . 
     The residual codebook storage unit  43  decodes the frame-based residual code, supplied from the demultiplexer  41 , into residual signals, based on the same codebook as that used in obtaining the residual code, to send the so decoded signals to a speech synthesis filter  44 . 
     Similarly to, for example, the speech synthesis filter  29  shown in  FIG. 1 , the speech synthesis filter  44  is an IIR type digital filter, and proceeds to filtering the residual signals from the residual codebook storage unit  43 , as input signals, using the linear prediction coefficients from the filter coefficient decoder  42  as tap coefficients of the IIR filter, to generate the synthesized sound, which then is routed to a tap generator  45 . 
     From sampled values of the synthesized speech, supplied from the speech synthesis filter  44 , the tap generator  45  extracts what is to be prediction taps used in prediction calculations in a prediction unit  49  which will be explained subsequently. That is, the tap generator  45  uses, as prediction taps, the totality of sampled values of the synthesized sound of a frame of interest, that is a frame the prediction values of the high quality speech of which are being found. The tap generator  45  routes the prediction taps to a prediction unit  49 . 
     The tap generator  46  extracts what are to become class taps from the frame- or subframe-based A code and residual code, supplied from the demultiplexer  41 . That is, the tap generator  46  renders the totality of the A code and the residual code the class taps, and routes the class taps to a classification unit  47 . 
     The pattern for constituting the prediction tap or class tap is not limited to the aforementioned pattern. 
     Meanwhile, the tap generator  46  is able to extract the class taps not only from the A and residual codes, but also from the linear prediction coefficients, output by the filter coefficient decoder  42 , residual signals output by the residual codebook storage unit  43  and from the synthesized sound output by the speech synthesis filter  44 . 
     Based on the class taps from the tap generator  46 , the classification unit  47  classifies the speech, more precisely sampled values of the speech, of the frame of interest, and outputs the resulting class code corresponding to the so obtained class to a coefficient memory  48 . 
     It is possible for the classification unit  47  to output a bit string itself forming the A code and the residual code of the frame of interest as the class tap. 
     The coefficient memory  48  holds class-based tap coefficients, obtained on carrying out the learning in the learning device of  FIG. 6 , which will be explained subsequently. The coefficient memory  48  outputs the tap coefficients stored in an address associated with the class code output by the classification unit  47  to the prediction unit  49 . 
     If N samples of high sound quality are found for each frame, N sets of tap coefficients are required in order to find N speech samples for the frame of interest by the predictive calculations of the equation (6). Thus, in the present case, N sets of tap coefficients are stored in the coefficient memory  48  for the address associated with one class code. 
     The prediction unit  49  acquires the prediction taps output by the tap generator  45  and the tap coefficients output by the coefficient memory  48  and, using the prediction taps and tap coefficients, performs linear predictive calculations (sum of product calculations) shown in the equation (6) to find predicted values of the high sound quality speech of the frame of interest to output the resulting values to a D/A converter  50 . 
     The coefficient memory  48  outputs N sets of tap coefficients for finding N samples of the speech of the frame of interest, as described above. Using the prediction taps of the respective samples and the set of tap coefficients corresponding to the sampled values, the prediction unit  49  carries out the sum-of-product processing of the equation (6). 
     The D/A converter  50  D/A converts the speech, more precisely predicted values of the speech, from the prediction unit  49 , from digital signals into corresponding analog signals, to send the resulting signals to the loudspeaker  51  as output. 
       FIG. 4  shows an illustrative structure of the speech synthesis filter  44  shown in  FIG. 3 . 
     In  FIG. 4 , the speech synthesis filter  44  uses p-dimensional linear prediction coefficients and is made up of a sole adder  61 , P delay circuits (D)  62   1  to  62   P  and P multipliers  63   1  to  63   P . 
     In the multipliers  63   1  to  63   P  are set P-dimensional linear prediction coefficients α 1 , α 2 , . . . , α p , sent from the filter coefficient decoder  42 , respectively, whereby the speech synthesis filter  44  carries out the calculations in accordance with the equation (4) to generate the synthesized sound. 
     That is, the residual signals e, output by the residual codebook storage unit  43 , are sent via adder  61  to the delay circuit  62   p , which delay circuit  62   p  delays the input signal thereto by one sample of the residual signals to output the delayed signal to a downstream side delay circuit  62   p+1  and to the multiplier  63   p . This multiplier  63   p  multiplies the output of the delay circuit  62   p  with the linear prediction coefficients α p  stored therein to output the resulting product to the adder  61 . 
     The adder  61  adds all outputs of the multipliers  63   1  to  63   p  and the residual signals e and sums the result of the addition to the delay circuit  62   1  while outputting it as being the result of speech synthesis (synthesized sound). 
     Referring to the flowchart of  FIG. 5 , the speech synthesis of the speech synthesis device of  FIG. 3  is now explained. 
     The demultiplexer  41  sequentially separates frame-based A code and residual code to send the separated codes to the filter coefficient decoder  42  and to the residual codebook storage unit  43 . The demultiplexer  41  sends the A code and the residual code to the tap generator  46 . 
     The filter coefficient decoder  42  sequentially decodes the frame-based A code, supplied thereto from the demultiplexer  41 , to send the resulting decoded coefficients to the speech synthesis filter  44 . The residual codebook storage unit  43  sequentially decodes the frame-based residual codes, supplied from the demultiplexer  41 , into residual signals, which are then sent to the speech synthesis filter  44 . 
     Using the residual signal and the linear prediction coefficients, supplied thereto, the speech synthesis filter  44  carries out the processing in accordance with the equation (4) to generate the synthesized speech of the frame of interest. This synthesized sound is sent to the tap generator  45 . 
     The tap generator  45  sequentially renders the frame of the synthesized sound, sent thereto, a frame of interest and, at step S 1 , generates prediction taps from sample values of the synthesized sound supplied from the speech synthesis filter  44 , to output the so generated prediction taps to the prediction unit  49 . At step S 1 , the tap generator  46  generates the class taps from the A code and the class taps from the A code and the residual code supplied from the demultiplexer  41  to output the so generated class taps to the classification unit  47 . 
     At step S 2 , the classification unit  47  carries out the classification, based on the class taps, supplied from the tap generator  46 , to send the resulting class codes to the coefficient memory  48 . The program the moves to step S 3 . 
     At step S 3 , the coefficient memory  48  reads out the tap coefficients, supplied from the address corresponding to the class codes supplied from the classification unit  47 , to send the resulting tap coefficients to the prediction unit  49 . 
     The program then moves to step S 4  where the prediction unit  49  acquires tap coefficients output by the coefficient memory  48  and, using the tap coefficients and the prediction taps from the tap generator  45 , carries out the sum-of-product processing shown in the equation (6) to produce predicted values of the high sound quality speech of the frame of interest. The high sound quality speech is sent to and output from the loudspeaker  51  via prediction unit  49  and D/A converter  50 . 
     If the speech of the high sound quality of the frame of interest has been acquired at the prediction unit  49 , the program moves to step S 5  where it is verified whether or not there is any frame to be processed as the frame of interest. If it is verified that there is still a frame to be processed as the frame of interest, the program reverts to step S 1  and repeats similar processing with the frame to be the next frame of interest as a new frame of interest. If it is verified at step S 5  that there is no frame to be processed as the frame of interest, the speech synthesis processing is terminated. 
     Referring to  FIG. 6 , an instance of a learning device for effecting the learning processing of the tap coefficients to be stored in the coefficient memory  48  of  FIG. 3  is now explained. 
     The learning device shown in  FIG. 6  is supplied with digital speech signals for learning, from one preset frame to another. These digital speech signals for learning are sent to an LPC analysis unit  71  and to a prediction filter  74 . The digital speech signals for learning are also supplied as teacher data to a normal equation addition circuit  81 . 
     The LPC analysis unit  71  sequentially renders the frame of the speech signals, supplied thereto, a frame of interest, and LPC-analyzes the speech signals of the frame of interest to find p-dimensional linear prediction coefficients which are then sent to the prediction filter  74  and to a vector quantizer  72 . 
     The vector quantizer  72  holds a codebook, associating the code vectors, having linear prediction coefficients as components, with the codes Based on the codebook, the vector quantizer  72  vector-quantizes the feature vectors, constituted by the linear prediction coefficients of the frame of interest from the LPC analysis unit  71 , and sends the A code, obtained as a result of the vector quantization, to a filter coefficient decoder  73  and to a tap generator  79 . 
     The filter coefficient decoder  73  holds the same codebook as that held by the vector quantizer  72  and, based on the codebook, decodes the A code from the vector quantizer  72  into linear prediction coefficients which are routed to a speech synthesis filter  77 . The filter coefficient decoder  42  of  FIG. 3  is constructed similarly to the filter coefficient decoder  73  of  FIG. 6 . 
     The prediction filter  74  carries out the processing, in accordance with the aforementioned equation (1), using the speech signals of the frame of interest, supplied thereto, and the linear prediction coefficients from the LPC analysis unit  71 , to find the residual signals of the frame of interest, which then are sent to vector quantizer  75 . 
     If the Z-transforms of s n  and e n  in the equation (1) are expressed as S and E, respectively, the equation (1) may be represented by the following equation:
 
 E= (1+α 1   z   −1 +α 2   z   −2 + . . . +α P   z   −P ) S.   (14)
 
     The prediction filter  74  for finding the residual signal e from the equation (14) may be constructed as a digital filter of the FIR (finite impulse response) type. 
       FIG. 7  shows an illustrative structure of the prediction filter  74 . 
     The prediction filter  74  is fed with p-dimensional linear prediction coefficients from the LPC analysis unit  71 , so that the prediction filter  74  is made up of p delay circuits D  91   1  to  91   p , p multipliers  92   1  to  92   p  and one adder  93 . 
     In the multipliers  92   1  to  92   p  are set p-dimensional linear prediction coefficients α 1 , α 2 , . . . , α p  supplied from the LPC analysis unit  71 . 
     On the other hand, the speech signals s of the frame of interest are sent to a delay circuit  91   1  and to an adder  93 . The delay circuit  91   p  delays the input signal thereto by one sample of the residual signals to output the delayed signal to the downstream side delay circuit  91   p+1  and to the operating unit  92   p . The multiplier  92   p  multiplies the output of the delay circuit  91   p  with the linear prediction coefficients, stored therein, to send the resulting product value to the adder  93 . 
     The adder  93  sums all of the outputs of the multipliers  92   1  to  92   p  to the speech signals s to send the results of addition as the residual signals e. 
     Returning to  FIG. 6 , the vector quantizer  75  holds a codebook, associating sample values of the residual signals as components, with the codes. Based on this codebook, residual vectors formed by the sample values of the residual signals of the frame of interest, from the prediction filter  74 , are vector quantized, and the residual codes, obtained as a result of the vector quantization, are sent to a residual codebook storage unit  76  and to the tap generator  79 . 
     The residual codebook storage unit  76  holds the same codebook as that held by the vector quantizer  75  and, based on the codebook, decodes the residual code from the vector quantizer  75  into residual signals which are routed to the speech synthesis filter  77 . The residual codebook storage unit  43  of  FIG. 3  is constructed similarly to the residual codebook storage unit  76  of  FIG. 6 . 
     A speech synthesis filter  77  is an IIR filter constructed similarly to the speech synthesis filter  44  of  FIG. 3 , and filters the residual signal from the residual signal storage unit  75  as an input signal, with the linear prediction coefficients from the filter coefficient decoder  73  as tap coefficients of the IIR filter, to generate the synthesized sound, which then is routed to a tap generator  78 . 
     Similarly to the tap generator  45  of  FIG. 3 , the tap generator  78  forms prediction taps from the linear prediction coefficients, supplied from the speech synthesis filter  77  to send the so formed prediction taps to the normal equation addition circuit  81 . Similarly to the tap generator  46  of  FIG. 3 , the tap generator  79  forms class taps from the A code and the residual code, sent from the vector quantizers  72  to  75 , to send the class taps to a classification unit  80 . 
     Similarly to the classification unit  47  of  FIG. 3 , the classification unit  80  carries out the classification, based on the class taps, supplied thereto, to send the resulting class codes to the normal equation addition circuit  81 . 
     The normal equation addition circuit  81  sums the speech for learning, which is the high sound quality speech of the frame of interest, as teacher data, to an output of the synthesized sound from the speech synthesis filter  77  forming the prediction taps as pupil data from the tap generator  78 . 
     Using the prediction taps (pupil data), supplied from the classification unit  80 , the normal equation addition circuit  81  carries out the reciprocal multiplication of the pupil data, as components in a matrix A of the equation (13) (x in x im ), and operations equivalent to summation (Σ). 
     Using the pupil data, that is sampled values of the synthesized sound output from the speech synthesis filter  77 , and teacher data, that is sampled values of the high sound quality speech of the frame of interest, the normal equation addition circuit  81  carries out the processing equivalent to multiplication (x in y i ), and summation (Σ) of the pupil data and the teacher data, as components in the vector v of the equation (13), for each class corresponding to the class code supplied from the classification unit  80 . 
     The normal equation addition circuit  81  carries out the above summation, using all of the speech frames for learning, supplied thereto, to establish the normal equation, shown in  FIG. 13 , for each class. 
     A tap coefficient decision circuit  82  solves the normal equation, generated in the normal equation addition circuit  81 , from class to class, to find tap coefficients for the respective classes. The tap coefficients, thus found, are sent to the address associated with each class of the memory  83 . 
     Depending on the speech signals, provided as speech signals for learning, there are occasions wherein, in a class or classes, a number of the normal equations required to find tap coefficients cannot be produced in the normal equation addition circuit  81 . For such class(es), the tap coefficient decision circuit  82  outputs default tap coefficients. 
     The coefficient memory  83  memorizes the class-based tap coefficients, supplied from the tap coefficient decision circuit  82 , in an address associated with the class. 
     Referring to the flowchart of  FIG. 8 , the learning processing by the learning device of  FIG. 6  is now explained. 
     The learning device is fed with speech signals for learning, which are sent to both the LPC analysis unit  71  and to the prediction filter  74 , while being sent as teacher data to the normal equation addition circuit  81 . At step S 11 , pupil data are generated from the speech signals for learning. 
     That is, the LPC analysis unit  71  sequentially renders the frames of the speech signals for learning the frames of interest and LPC-analyzes the speech signals of the frames of interest to find p-dimensional linear prediction coefficients which are sent to the vector quantizer  72 . The vector quantizer  72  vector-quantizes the feature vectors formed by the linear prediction coefficients of the frame of interest, from the LPC analysis unit  71 , and sends the A code resulting from the vector quantization to the filter coefficient decoder  73  and to the tap generator  79 . The filter coefficient decoder  73  decodes the A code from the vector quantizer  72  into linear prediction coefficients which are sent to the speech synthesis filter  77 . 
     On the other hand, the prediction filter  74 , which has received the linear prediction coefficients of the frame of interest from the LPC analysis unit  71 , carries out the processing of the equation (1), using the linear prediction coefficients and the speech signals for learning of the frame of interest, to find the residual signals of the frame of interest to send the so found residual signals to the vector quantizer  75 . The vector quantizer  75  vector-quantizes the residual vector formed by the sample values of the residual signals of the frame of interest from the prediction filter  74  to send the residual code obtained on vector quantization to the residual codebook storage unit  76  and to the tap generator  79 . The residual codebook storage unit  76  decodes the A code from the vector quantizer  75  into linear prediction coefficients which are then supplied to the speech synthesis filter  77 . 
     On receipt of the linear prediction coefficients and the residual signals, the speech synthesis filter  77  performs speech synthesis, using the linear prediction coefficients and the residual signals, to output the resulting synthesized signals as pupil data to the tap generator  78 . 
     The program then moves to step S 12  where the tap generator  78  generates prediction taps from the synthesized sound supplied from the speech synthesis filter  77 , while the tap generator  79  generates class taps from the code A from the vector quantizer  72  and from the residual code from the vector quantizer  75 . The prediction taps are sent to the normal equation addition circuit  81 , whilst the class taps are routed to the classification unit  80 . 
     At step S 13 , the classification unit  80  then performs classification based on the class taps from the tap generator  79  to route the resulting class code to the normal equation addition circuit  81 . 
     The program then moves to step S 14  where the normal equation addition circuit  81  carries out the aforementioned addition to the matrix A and the vector v of the equation (13), for the sample values of the speech of the high sound quality of the frame of interest as teacher data supplied thereto, and the prediction taps, more precisely the sampled values of the synthesized sound making up the prediction taps, as pupil data from the tap generator  78  for the class supplied from the classification unit  80 . The program then moves to step S 15 . 
     At step S 15 , it is verified whether or not there are any speech signals for learning to be processed as the frame of interest. If it is verified at step S 15  that there are any speech signals for learning to be processed as the frame of interest, the program reverts to step S 11  to repeat the similar processing, with the sequentially next frames as the new frame of interest. 
     If it is found at step S 15  that there is no speech signal for learning of the frame to be processed as the frame of interest, that is if a normal equation has been obtained for each class in the normal equation addition circuit  81 , the program moves to step S 16  where the tap coefficient decision circuit  82  solves the normal equation generated from class to class to find the tap coefficients for each class. The so found tap coefficients are sent to the address associated with each class in a coefficient memory  83  for storage therein to terminate the processing. 
     The class-based tap coefficients, thus stored in the coefficient memory  83 , are stored in this manner in the coefficient memory  48  of  FIG. 3 . 
     Thus, since the tap coefficients stored in the coefficient memory  48  of  FIG. 3  are found in this manner by carrying out the learning in such a manner that the prediction error of the prediction values of the speech of the high sound quality, that is the square error, will be statistically minimum, the speech output by the prediction unit  49  of  FIG. 3  is of high sound quality in which the distortion of the synthesized sound output by the speech synthesis filter  44  has been reduced or eliminated. 
     Meanwhile, if, in the speech synthesis device of  FIG. 3 , the class taps are to be extracted by e.g., the tap generator  46  from the linear prediction coefficients or the residual signals, it is necessary to have the tap generator  79  of  FIG. 6  extract the similar class taps from the linear prediction coefficients output by the filter coefficient decoder  73  and from the residual signals output by the residual codebook storage unit  76 . However, if class taps are extracted even from e.g., the linear prediction coefficients, the number of the taps is increased. So, the classification preferably is to be carried out by compressing the class taps by, for example, the vector quantization. Meanwhile, if the classification is to be performed solely by the residual code and the A code, the load needed in classification processing may be relieved because the array of bit strings of the residual code and the A code can directly be used as the class code. 
     An instance of the transmission system embodying the present invention is explained with reference to  FIG. 9 . The system herein means a set of logically arrayed plural devices, while it does not matter whether or not the respective devices are in the same casing. 
     In the transmission system shown in  FIG. 9 , the portable telephone sets  101   1 ,  101   2  perform radio transmission and receipt with base stations  102   1 ,  102   2 , respectively, while the base stations  102   1 ,  102   2  perform transmission and receipt with an exchange station  103  to enable speech transmission and receipt of speech between the portable telephone sets  101   1 ,  101   2  with the aid of the base stations  102   1 ,  102   2  and the exchange station  103 . The base stations  102   1 ,  102   2  may be the same as or different from each other. 
     The portable telephone sets  101   1 ,  101   2  are referred to below as a portable telephone set  101 , unless there is specified necessity for making distinction between the sets. 
       FIG. 10  shows an illustrative structure of the portable telephone set  101  shown in  FIG. 9 . 
     An antenna  111  receives electrical waves from the base stations  102   1 ,  102   2  to send the received signals to a modem  112  as well as to send the signals from the modem  112  to the base stations  102   1 ,  102   2  as electrical waves. The modem  112  demodulates the signals from the antenna  111  to send the resulting code data explained with reference to  FIG. 1  to a receipt unit  114 . The modem  112  also is configured for modulating the code data from the transmitter  113  as shown in  FIG. 1  and sends the resulting modulated signal to the antenna  111 . The transmitter  113  is configured similarly to the transmitter shown in  FIG. 1  and codes the user&#39;s speech input thereto into code data which is supplied to the modem  112 . The receipt unit  114  receives the code data from the modem  112  to decode and output the speech of high sound quality similar to that obtained in the speech synthesis device of  FIG. 3 . 
     That is,  FIG. 11  shows an illustrative structure of the receipt unit  114  of  FIG. 10 . In the drawing, parts or components corresponding to those shown in  FIG. 2  are depicted by the same reference numerals and are not explained specifically. 
     A tap generator  121  is fed with the synthesized sound output by a speech synthesis unit  29 . From the synthesized sound, the tap generator  121  extracts what are to be prediction taps (sampled values), which are then routed to a prediction unit  125 . 
     A tap generator  122  is fed with frame-based or subframe-based L, G and A codes, output by a channel decoder  21 . The tap generator  122  is also fed with residual signals from the operating unit  28 , while also being fed with linear prediction coefficients from a filter coefficient decoder  25 . The tap generator  122  generates what are to be class taps, from the L, G, I and A codes, residual signals and the linear prediction coefficients, supplied thereto, to route the extracted class taps to a classification unit  123 . 
     The classification unit  123  carries out classification, based on the class taps supplied from the tap generator  122 , to route the class codes as the being the results of the classification to a coefficient memory  124 . 
     If the class taps are formed from the L, G, I and A codes, residual signals and the linear prediction coefficients, and classification is carried out based on these class taps, the number of the classes obtained on classification tends to be enormous. Thus, it is also possible for the classification unit  123  to output the codes, obtained on vector quantization of the vectors having the L, G, I and A codes, residual signals and the linear prediction coefficients, as components, as being the results of the classification. 
     The coefficient memory  124  memorizes the class-based tap coefficients, obtained on learning by the learning device of  FIG. 12 , as later explained, and routes the tap coefficients, stored in the address associated with the class code output by the classification unit  123 , to the prediction unit  125 . 
     Similarly to the prediction unit  49  of  FIG. 3  the prediction unit  125  acquires the prediction taps, output by the tap generator  121 , and tap coefficients, output by the coefficient memory  124 , and performs the linear predictive calculations of the equation (6), using the prediction taps and the tap coefficients. The prediction unit  125  finds the speech of high sound quality of the frame of interest, more precisely, prediction values thereof, and performs the linear predictive calculations shown in the equation (6). In this manner, the prediction unit  125  finds the speech of high sound quality of the frame of interest, more precisely, prediction values thereof, and sends the so found out values as being the result of speech decoding to a D/A converter  30 . 
     The receipt unit  114 , designed as described above, performs the processing basically the same as the processing complying with the flowchart of  FIG. 5  to output the synthesized sound of high sound quality as being the result of speech decoding. 
     That is, the channel decoder  21  separates the L, G, I and A codes, from the code data, supplied thereto, to send the so separated codes to the adaptive codebook storage unit  22 , gain decoder  23 , excitation codebook storage unit  24  and to the filter coefficient decoder  25 , respectively. The L, G, I and A codes are also sent to the tap generator  122 . 
     The adaptive codebook storage unit  22 , gain decoder  23 , excitation codebook storage unit  24  and the operating units  26  to  28  perform the processing similar to that performed in the adaptive codebook storage unit  9 , gain decoder  10 , excitation codebook storage unit  11  and in the operating units  12  to  14  of  FIG. 1  to decode the L, G and I codes to residual signals e. These residual signals are routes to the speech synthesis unit  29  and to the tap generator  122 . 
     As explained with reference to  FIG. 1 , the filter coefficient decoder  25  decodes the A codes, supplied thereto, into linear prediction coefficients, which are routed to the speech synthesis unit  29  an to the tap generator  122 . Using the residual signals from the operating unit  28  and the linear prediction coefficients supplied from the filter coefficient decoder  25 , the speech synthesis unit  29  synthesizes the speech, and sends the resulting synthesized sound to the tap generator  121 . 
     Using a frame of the synthesized sound, output from the speech synthesis unit  29 , as the frame of interest, the tap generator  121  at step S 1  generates prediction taps, from the synthesized sound of the frame of interest, and sends the so generated prediction taps to the prediction unit  125 . At step S 1 , the tap generator  122  generates class taps, from the L, G, I and A codes, residual signals and the linear prediction coefficients, supplied thereto, and sends these to the classification unit  123 . 
     The program then moves to step S 2  where the classification unit  123  carries out the classification based on the class taps sent from the tap generator  122  to send the resulting class codes to the classification unit  124 . The program then moves to step S 3 . 
     At step S 3 , the coefficient memory  124  reads out tap coefficients, corresponding to the class codes, supplied form the classification unit  123 , to send the so read out tap coefficients to the prediction unit  125 . 
     The program moves to step S 4  where the prediction unit  125  acquires tap coefficients for the residual signals output by the coefficient memory coefficient memory  124 , and carries out sum-of-products processing in accordance with the equation (6), using the tap coefficients and the prediction taps from the tap generator  121 , to acquire prediction values of the speech of high sound quality of the frame of interest. 
     The speech of high sound quality, obtained as described above, is sent from the prediction unit  125  through the D/A converter  30  to the loudspeaker  31  which then outputs the speech of the high sound quality. 
     After the processing at step S 4 , the program moves to step S 5  where it is verified whether or not there is any frame to be processed as the frame of interest. If it is found that there is any such frame, the program reverts to step S 1 , where the similar processing is repeated with the frame to be the next frame of interest as being the new frame of interest. If it is found at step S 5  that there is no frame to be processed as being the frame of interest, the processing is terminated. 
       FIG. 12  shows an instance of a learning device adapted for carrying out the processing of learning tap coefficients memorized in the coefficient memory  124  of  FIG. 11 . 
     In the learning device of  FIG. 12 , the components from a microphone  201  to a code decision unit  215  are constructed similarly to the microphone  1  to the code decision unit  15  of  FIG. 1 . The microphone  1  is fed with speech signals for learning. So, the components from a microphone  201  to a code decision unit  215  perform the same processing on the speech signals for learning as that in  FIG. 1 . 
     A tap generator  131  is fed with the synthesized sound output by a speech synthesis filter  206  when a minimum square error decision unit  208  has verified the square error to be smallest. Meanwhile, a tap generator  132  is fed with the L, G, I and A codes output when the definite signal has been received by the code decision unit  215  from the minimum square error decision unit  208 . The tap generator  132  is also fed with the linear prediction coefficients, as components of code vectors (centroid vectors) corresponding to the A code as the results of vector quantization of the linear prediction coefficients obtained at an LPC analysis unit  204 , output by the vector quantizer  205 , and with residual signals output by the operating unit  214 , that prevail when the square error in the minimum square error decision unit  208  has become minimum. A normal equation summation circuit  134  is fed with speech output by an A/D converter  202  as teacher data. 
     From the synthesized sound, output by a speech synthesis filter  206 , the tap generator  131  generates the same prediction taps as those of the tap generator  121  of  FIG. 1 , and routes the so generated prediction taps as pupil data to the normal equation summation circuit  134 . 
     From the L, G, I sans A codes from the code decision unit  215 , linear prediction coefficients, issued by the vector quantizer  205 , from the residual signals and from the operating unit  214 , the tap generator  132  forms the same class taps as those of the tap generator  122  of  FIG. 11  to send the so formed class taps to the classification unit  133 . 
     Based on the class taps from the tap generator  132 , a classification unit  133  carries out the same classification as that performed by the classification unit  123  and routes the resulting class code to the normal equation summation circuit  134 . 
     The normal equation summation circuit  134  receives the speech from the A/D converter  202  as teacher data, while receiving the prediction taps from the tap generator  131  as pupil data. The normal equation summation circuit  134  then performs the similar summation to that performed by the normal equation addition circuit  81  of  FIG. 6  to establish the normal equation shown as in the equation (13) for each class. 
     A tap coefficient decision circuit  135  solves the normal equation, generated in the normal equation addition circuit  134  from class to class, to find tap coefficients for the respective classes. The tap coefficients, thus found, are sent to the address associated with each class of a coefficient memory  136 . 
     Depending on the speech signals, provided as speech signals for learning, there are occasions wherein, in a class or classes, a number of the normal equations required to find tap coefficients cannot be produced in the normal equation addition circuit  134 . For such class(es), the tap coefficient decision circuit  135  outputs default tap coefficients. 
     The coefficient memory  136  memorizes the class-based linear prediction coefficients and residual signals, supplied from the tap coefficient decision circuit  135 . 
     The above-described learning device basically performs the processing similar to that conforming to the flowchart shown in  FIG. 8  to find tap coefficients for producing the synthesized sound of high sound quality. 
     The learning device is fed with speech signals for learning. At step S 11 , teacher data and pupil data are generated from the speech signals for learning. 
     That is, the speech signals for learning are fed to the microphone  201 . The components from the microphone  201  to the code decision unit  215  perform the processing similar to that performed by the components from the microphone  1  to the code decision unit  15  of  FIG. 1 . 
     The result is that the speech of the digital signals, obtained by the A/D converter  202 , are sent as teacher data to the normal equation summation circuit  134 . If it is verified that the square error has become smallest in the minimum square error decision unit  208 , the synthesized sound, output by the speech synthesis filter  206 , is sent as pupil data to the tap generator  131 . 
     When the linear prediction coefficients output by the vector quantizer  205  are such that the square error as found by the minimum square error decision unit  208  is minimum, the L, G, I and A codes, output by the code decision unit  215 , and the residual signals output by the operating unit  214 , are sent to the tap generator  132 . 
     The program then moves to step S 12  where the tap generator  131  generates prediction taps from the synthesized sound of the frame of interest, with the frame of the synthesized sound supplied as pupil data from the speech synthesis filter  206  to send the so generated prediction taps to the normal equation summation circuit  134 . At step S 12 , the tap generator  132  generates class taps from the L, G, I and A codes, linear prediction coefficients and the residual signals, supplied thereto, to send the so generated class taps to the classification unit  133 . 
     After the processing at step S 12 , the program moves to step S 13  where the classification unit  133  performs classification based on the class taps from the tap generator  132  to send the resulting class codes to the normal equation summation circuit  134 . 
     The program then moves to step S 14  where the normal equation summation circuit  134  performs the aforementioned summation of the matrix A and the vector v of the equation (13), for the speech signals for learning, as the speech of the high sound quality of the frame of interest from the A/D converter  202 , as teacher data and for prediction taps from the tap generator  132 , as pupil data, from one class code from the classification unit  133  to another. The program then moves to step S 15 . 
     At step S 15 , it is verified whether or not there is any frame to be processed as the frame of interest. If it is found at step S 15  that there is still a frame to be processed as the frame of interest, the program reverts to step S 11  where the processing similar to that described above is repeated with the sequentially next frame as being new frames of interest. 
     If it is found at step S 15  that there is no frame to be processed as being the frame of interest, that is if the normal equation has been obtained for each class in the normal equation summation circuit  134 , the program moves to step S 16  where the tap coefficient decision circuit  135  solves the normal equation generated for each class to find the tap coefficients from class to class to send the so found tap coefficients to the address associated with each class to terminate the processing. 
     The class-based tap coefficients stored in the coefficient memory  136  are stored in the coefficient memory coefficient memory  124  of  FIG. 11 . 
     Consequently, the tap coefficients stored in the coefficient memory  124  of  FIG. 11  have been found by carrying out the learning such that the prediction errors (square errors) of the predicted speech values of high sound quality obtained on linear predictive calculations will be statistically minimum, so that the speech output by the prediction unit  125  of  FIG. 11  is of high sound quality. 
     The above-described sequence of operations may be carried out by handwave or by software. If the sequence of operations is carried out by software, the program forming the software is installed on e.g., general-purpose computer. 
       FIG. 13  shows an illustrative structure of an embodiment of a computer on which to install the program adapted for executing the above-described sequence of operations. 
     It is possible for the program to be pre-recorded on a hard disc  305  or a ROM  303  as a recording medium enclosed in a computer. 
     Alternatively, the program may be transiently or permanently stored in a removable recording medium  311 , such as CD-ROM (Compact Disc Read Only memory), MO (magneto-optical) disc, DVD (Digital Versatile Disc), magnetic disc or a semiconductor memory. Such removable recording medium  311  may be furnished as a so-called package software. 
     Meanwhile, the program may not only be installed from the above-described removable recording medium  311  on a computer but also transferred over a radio route to the computer from a downloading site, over a network, such as LAN (Local Area network) or Internet. The so transferred program on a communication unit  308  may be received by the communication unit  308  so as to be installed on an enclosed hard disc  305 . 
     The computer has enclosed therein a CPU (central processing unit)  302 . To this CPU  302  is connected an input/output interface  310  over a bus  301 . When a command is input to the CPU  302  over the input/output interface  310  by a user acting on an input unit  307 , such as a keyboard, mouse or microphone, the program loaded on the ROM (Read Only Memory) is executed. Alternatively, the CPU  302  loads a program, stored in the hard disc  305 , a program transmitted over the satellite or network, received by a communication unit  308  and installed on the hard disc  305 , or a program read out from the removable recording medium  311  loaded on the hard disc  305 , on a RAM (Random Access memory)  304  for execution. The CPU  302  now executes the processing in accordance with the above-described flowchart or the processing conforming to the above-described block diagram. The CPU  302  causes the processing results to be output over e.g., the input/output interface  310  from an output unit  306  formed by LCD (liquid crystal display) or a loudspeaker, transmitted from the communication unit  308  or recorded on the hard disc  305 . 
     The processing step for stating the program for executing the various processing operations by a computer need not be carried out chronologically in the order stated in the flowchart, but may be processed in parallel or batch-wise, such as parallel processing or object-wise processing. 
     The program may be processed by a sole computer or by plural computers in a distributed fashion. Moreover, the program may be transmitted to a remotely located computer for execution. 
     Although no particular reference has been made in the present invention as to which sort of the speech signals for learning is to be used, the speech signals for learning may not only be the speech uttered by a speaker or a musical number (music). With the above-described learning, such tap coefficients which will improve the sound quality of the speech are obtained if the speech uttered by a speaker is used, whereas, if the speech signals for learning are music numbers, such tap coefficients which will improve the sound quality of the speech are obtained which will improve the sound quality of the musical number. 
     In an embodiment shown in  FIG. 11 , the tap coefficients are pre-stored in the coefficient memory  124 . Alternatively, the tap coefficients to be stored in the coefficient memory  124  may also be downloaded in the portable telephone set  101  from the base station  102  or the exchange station  103  of  FIG. 9  or from a WWW (World Wide Web) server, not shown. That is, the tap coefficients suited to a sort of speech signals, such as those for the human speech or music, may be obtained on learning. Depending on the teacher or pupil data used for learning, such tap coefficients which will produce a difference in the sound quality of the synthesized sound may be acquired. So, these various tap coefficients may be stored in e.g., the base station  102  for the user to download the tap coefficients the or she desires. Such service of downloading the tap coefficients may be payable or charge-free. If the service of downloading the tap coefficients is to be payable, the fee as remuneration for the downloaded tap coefficients may be charged along with the call toll of the portable telephone set  101 . 
     The coefficient memory coefficient memory  124  may be formed by e.g., a memory card that can be mounted on or dismounted from the portable telephone set  101 . If, in this case, variable memory cards having stored thereon the above-described various tap coefficients are furnished, the memory cards holding the desired tap coefficients may be loaded and used on the portable telephone set  101 . 
     The present invention may be broadly applied in generating the synthesized sound from the code obtained on encoding by the CELP system, such as VSELP (Vector Sum Excited linear Prediction), PSI-CELP (Pitch Synchronous Innovation CELP), CS-ACELP (Conjugate Structure Algebraic CELP). 
     The present invention also is broadly applicable not only to such a case where the synthesized sound is generated from the code obtained on encoding by CELP system but also to such a case where residual signals and linear prediction coefficients are obtained from a given code to generate the synthesized sound. 
     In the above-described embodiment, the prediction values of residual signals and linear prediction coefficients are found by one-dimensional linear predictive calculations. Alternatively, these prediction values may be found by two- or higher dimensional predictive calculations. 
     Also, in the receipt unit shown in  FIG. 11  and in the learning device shown in  FIG. 12 , the class taps are generated based not only on the L, G, I and A codes, but also on linear prediction coefficients derived from the A codes and residual signals derived from the L, G and I codes. The class codes may also be generated from only one or a plural number of the L, G, I and A codes, such as, for example, from only the A code. If, for example, the class taps are formed only from the I code, the I code it self may be used as the class code. Since the VSELP system allocates 9 bits to the I code, the number of the classes is 512 (=2 9 ) if the I code is directly used as the class code. Meanwhile, each bit of the 9-bit I code has two sorts of signs, namely 1 and −1, it is sufficient if a bit which is −1 is deemed to be 0 if this I code is used as the class code. 
     In the CELP system, software interpolation bits or the frame energy may sometimes be included in the code data. In this case, the class taps may be formed by using software interpolation bits or the frame energy. 
     In Japanese Laying-Open Patent Publication H-8-202399, there is disclosed a method of passing the synthesized sound through a high range emphasizing filter to improve its sound quality. The present invention differs from the invention disclosed in the Japanese Laying-Open Patent Publication H-8-202399 e.g., in that the tap coefficients are obtained on learning and in that the tap coefficients used are determined from the results of the code-based classification. 
     Referring to the drawings, a modification of the present invention is explained in detail. 
       FIG. 14  shows a structure of a speech synthesis device embodying the present invention. This speech synthesis device is fed with code data multiplexed from the residual code and the A code obtained respectively on coding the residual signal and the linear prediction coefficients A sent to a speech synthesis filter  147 . The residual signals and the linear prediction coefficients are found from the residual and A codes, respectively, and routed to the speech synthesis filter  147  to generate the synthesized sound. 
     If the residual code is decoded into the residual signals based on the codebook which associates the residual signals with the residual code, the residual signals, obtained on decoding, are corrupted with errors, with the result that the synthesized sound is deteriorated in sound quality. Similarly, if the A code is decoded into linear prediction coefficients based on the codebook which associates the linear prediction coefficients with the A code, the decoded linear prediction coefficients are again corrupted with errors, thus deteriorating the sound quality of the synthesized sound. 
     So, in the speech synthesis device of  FIG. 14 , the predictive calculations are carried out using tap coefficients as found on learning to find prediction values for true residual signals and linear prediction coefficients and the synthesized sound of high sound quality is produced using these prediction values. 
     That is, in the speech synthesis device of  FIG. 14 , the linear prediction coefficients decoded are decoded to prediction values of true linear prediction coefficients using e.g., the classification adaptive processing. 
     The classification adaptive processing is made up by classification processing and adaptive processing. By the classification processing, the data is classified depending on data properties and adaptive processing is carried out from class to class, while the adaptive processing is carried out by a technique which is the same as that described above. So, reference may be had to the foregoing description, and detailed description is not made here for simplicity. 
     In the speech synthesis device, shown in  FIG. 14 , the decoded linear prediction coefficients are decoded into true linear prediction coefficients, more precisely prediction values thereof, whilst decoded residual signals are also decoded into true residual signals, more precisely prediction values thereof. 
     That is, a demultiplexer (DEMUX)  141  is fed with code data and separates the code data supplied into frame-based A code and residual code, which are routed to a filter coefficient decoder  142 A and a residual codebook storage unit  142 E, respectively. It should be noted that the A code and the residual code, included in the code data in  FIG. 14 , are obtained on vector quantization of linear prediction coefficients and residual signals, obtained in turn on LPC analysis of the speech in terms of a preset frame as unit, using a preset codebook. 
     The filter coefficient decoder  142 A decodes the frame-based A code, supplied from the demultiplexer  141 , into decoded linear prediction coefficients, based on the same codebook as that used in obtaining the A code, to route the resulting decoded linear prediction coefficients to the tap generator  143 A. 
     The residual codebook storage unit  142 E memorizes the same codebook as that used in obtaining the frame-based residual code, supplied from the demultiplexer  141 , and decodes the residual code from the demultiplexer into the decoded residual signals, based on the codebook, to route the so produced decoded residual signals to the tap generator  143 E. 
     From the frame-based decoded linear prediction coefficients, supplied from the filter coefficient decoder  142 A, the tap generator  143 A extracts what are to be class taps used in classification in a classification unit  144 A, and what are to be prediction taps used in predictive calculations in a prediction unit  146 , as later explained. That is, the tap generator  143 A sets the totality of the decoded linear prediction coefficients as prediction taps and class taps for the linear prediction coefficients. The tap generator  143 A sends the class taps pertinent to the linear prediction coefficients and the prediction taps to the classification unit  144 A and to the prediction unit  146 A, respectively. 
     From the frame-based decoded residual signals, the tap generator  143 E extracts what are to be class taps and what are to be prediction taps from the frame-based decoded residual signals supplied from the residual codebook storage unit  142 E. That is, the tap generator  143 E makes all sample values of the decoded residual signals of a frame being processed into class taps and prediction taps for the residual signals. The tap generator  143 E sends class taps pertinent to the residual signals and prediction taps to the classification unit  144 E and to the prediction unit  146 E, respectively. 
     The constituent pattern of the prediction taps and class taps are not limited to the above-mentioned patterns. 
     It should be noted that the may be designed to extract class taps and prediction taps of the linear prediction coefficients from both the decoded linear prediction coefficients and the decoded residual signals. The class taps and prediction patterns pertinent to the linear prediction coefficients may also be extracted by the tap generator  143 A from the A code and the residual code. The class taps and prediction patterns of the linear prediction coefficients may also be extracted from signals already output from the downstream side prediction units  146 A or  146 E or from the synthesized speech signals already output by the speech synthesis filter  147 . It is also possible for the tap generator  143 E to extract class and prediction taps pertinent to the residual signals in similar manner. 
     Based on the class taps pertinent to the linear prediction coefficients from the tap generator  143 A, the classification unit  144 A classifies the linear prediction coefficients of the frame, which is a frame of interest, and the prediction values of true linear prediction coefficients of which are to be found, and outputs the class code, corresponding to the resulting class, to a coefficient memory  145 A. 
     As the method for classification, ADRC (Adaptive Dynamic Range Coding), for example, may be employed. 
     In a method employing the ADRC, the decoded linear prediction coefficients forming class taps, are ADRC processed and, based on the resulting ADRC code, the class of the linear prediction coefficients of the frame of interest is determined. 
     In a K-bit ADRC, the maximum value MAX and the minimum value MIN of decoded linear prediction coefficients, forming class taps, are detected based on a local dynamic range of a set DR=MAX−MIN, and the decoded linear prediction coefficients, forming the class taps, are re-quantized into K bits. That is, the minimum value MIN is subtracted from the decoded linear prediction coefficients, forming the class taps, and the resulting difference value is divided by DR/2K. The respective decoded linear prediction coefficients, forming the class taps, obtained as described above, are arrayed in a preset sequence to form a bit string, which is output as an ADRC code. Thus, if the class taps are processed with e.g., one-bit ADRC, the minimum value MIN is subtracted from the respective decoded linear prediction coefficients, forming the class taps, and the resulting difference value is divided by the average value of the maximum value MAX and the minimum value MIN, whereby the respective decoded linear prediction coefficients are of one-bit values, by way of binary coding. The bit string, obtained on arraying the one-bit decoded linear prediction coefficients, is output as the ADRC code. 
     The string of values of decoded linear prediction coefficients, forming class taps, may directly be output as the class code to the classification unit  144 A. If the class taps are formed as p-dimensional linear prediction coefficients, and K bits are allocated to the respective decoded linear prediction coefficients, the number of different class codes, output by the classification unit  144 A, is (2 K ) k  which is an extremely large value exponentially proportionate to the number of bits K of the decoded linear prediction coefficients. 
     Thus, classification in the classification unit  144 A is preferably carried out after compressing the information volume of the class taps by e.g., the ADRC processing or vector quantization. 
     Similarly to the classification unit  144 A, the classification unit  144 E carries out classification of the frame of interest, based on the class taps supplied from the tap generator  143 E, to output the resulting class codes to the coefficient memory  145 E. 
     The coefficient memory  145 E holds tap coefficients pertinent to the class-based linear prediction coefficients, obtained on performing the learning in a learning device of  FIG. 17  as later explained, and outputs the tap coefficients, stored in an address associated with the class code output by the classification unit  144 A, to the prediction unit  146 A. 
     The coefficient memory  145 E holds tap coefficients pertinent to the class-based linear prediction coefficients, as obtained by carrying out the learning in the learning device of  FIG. 17 , and outputs the tap coefficients, stored in the address corresponding to the class code output by the classification unit  144 E, to the prediction unit  146 E. 
     If, in case p-dimensional linear prediction coefficients are to be found in each frame, the p-dimensional linear prediction coefficients are to be found by predictive calculations of the aforementioned equation (6), p sets of the tap coefficients are needed. Thus, in the coefficient memory  145 A, p sets of the tap coefficients are stored in an address associated with one class code. For the same reason, the same number of sets as that of the sample points of the residual signals in each frame is stored in the coefficient memory  145 E. 
     The prediction unit  146 A acquires prediction taps output by the tap generator  143 A and the tap coefficients output by the coefficient memory  145 A and, using these prediction and tap coefficients, performs the linear prediction calculations (sum-of-product processing), shown by the equation (6), to find the p-dimensional linear prediction coefficients of the frame of interest, more precisely the predicted values thereof, to send the so found out values to the speech synthesis filter  147 . 
     The prediction unit  146 E acquires the prediction taps, output by the tap generator  143 E, and the tap coefficients output by the coefficient memory  145 E. Using the so acquired prediction and tap coefficients, the prediction unit  146 E carries out the linear prediction calculations, shown by the equation (6), to find predicted values of the residual signals of the frame of interest to output the so found out values to the speech synthesis filter  147 . 
     The coefficient memory  145 A outputs P sets of tap coefficients for finding predicted values of the p-dimensional linear prediction coefficients forming the frame of interest. On the other hand, the prediction unit  146 A executes the sum-of-products processing of the equation (6), using the prediction taps, and the sets of the tap coefficients corresponding to the number of the dimensions, in order to find the linear prediction coefficients of the respective dimensions. The same holds for the prediction unit  146 E. 
     Similarly to the speech synthesis unit  29 , explained with reference to  FIG. 1 , the speech synthesis filter  147  is an IIR type digital filter, and carries out the filtering of the residual signals from the prediction unit  146 E as input signal, with the linear prediction coefficients from the prediction unit  146 A as tap coefficients of the IIR filter, to generate the synthesized sound, which is input to a D/A converter  148 . The D/A converter  148  D/A converts the synthesized sound from the speech synthesis filter  147  from the digital signals into the analog signals, which are sent to and output at a loudspeaker  149 . 
     In  FIG. 14 , class taps are generated in the tap generators  143 A,  143 E, classification based on these class taps is carried out in the classification units  144 A,  144 E and tap coefficients for the linear prediction coefficients and the residual signals corresponding to the class codes as being the results of the classification are acquired from the coefficient memories  145 A,  145 E. Alternatively, the tap coefficients of the linear prediction coefficients and the residual signals can be acquired as follows: 
     That is, the tap generators  143 A,  143 E, classification units  144 A,  144 E and the coefficient memories  145 A,  145 E are constructed as respective integral units. If the tap generators, classification units and the coefficient memories, constructed as respective integral units, are named a tap generator  143 , a classification unit  144  and a coefficient memory  145 , respectively, the tap generator  143  is caused to form class taps from the decoded linear prediction coefficients and decoded residual signals, while the classification unit  144  is caused to perform classification based on the class taps to output one class code. The coefficient memory  145  is caused to hold sets of tap coefficients for the decoded linear prediction coefficients and tap coefficients for the residual signals, and is caused to output sets of the tap coefficients for each of the linear prediction coefficients and the residual signals stored in the address associated with the class code output by the classification unit  144 . The prediction units  146 A,  146 E may be caused to carry out the processing based on the tap coefficients pertinent to the linear prediction coefficients output as sets from the coefficient memory  145  and on the tap coefficients for the residual signals. 
     If the tap generators  143 A,  143 E, classification units  144 A,  144 E and the coefficient memories  145 A,  145 E are constructed as respective separate units, the number of classes for the linear prediction coefficients is not necessarily the same as the number of classes for the residual signals. In case of construction as the integral units, the number of the classes of the linear prediction coefficients is the same as that of the residual signals. 
       FIG. 15  shows a specified structure of the speech synthesis filter  147  making up the speech synthesis device shown in  FIG. 14 . 
     The speech synthesis filter  147  uses the p-dimensional linear prediction coefficients, as shown in  FIG. 15 , and hence is made up by a sole adder  151 , p delay circuits (D)  152   1  to  152   p  and p multipliers  153   1  to  153   p . 
     In the multipliers  153   1  to  153   p  are set p-dimensional linear prediction coefficients α 1 , α 2 , . . . , α p , supplied from the prediction unit  146 A, whereby the speech synthesis filter  147  performs calculations in accordance with the equation (4) to generate the synthesized sound. 
     That is, the residual signals, output by the prediction unit  146 E, are sent to a delay circuit  152   1  through adder  151 . The delay circuit  152   p  delays the input signal by one sample of the residual signals to output the delayed signal to the downstream side delay circuit  152   p+1  and to the multiplier  153   p . The multiplier  153   p  multiplies the output of the delay circuit  12   p  with the linear prediction coefficient α p  set thereat to send the resulting product value to the adder  151 . 
     The adder  151  sums all outputs of the multipliers  153   1  to  153   p  and the residual signals e to send the resulting sum to the delay circuit  12   1  and to output the sum as the result of speech synthesis (resulting sound signal). 
     Referring to the flowchart of  FIG. 16 , the speech synthesis processing of  FIG. 14  is explained. 
     The demultiplexer  141  sequentially separates frame-based A code and residua code, from the code data, supplied thereto, to send the separated codes to the filter coefficient decoder  142 A and to the residual codebook storage unit  142 E. 
     The filter coefficient decoder  142 A sequentially decodes the frame-based A code, supplied from the demultiplexer  141 , into decoded linear prediction coefficients, which are supplied to the tap generator  143 A. The residual codebook storage unit  142 E sequentially decodes the frame-based residual codes, supplied from the demultiplexer  141 , into decoded residual signals, which are sent to the tap generator  143 E. 
     The tap generator  143 A sequentially renders the frames of the decoded linear prediction coefficients supplied thereto the frames of interest. The tap generator  143 A at step S 101  generates the class taps and the prediction taps from the decoded linear prediction coefficients supplied from the filter coefficient decoder  142 A. At step S 101 , the tap generator  143 E also generates class taps and prediction taps from the decoded residual signals supplied from the residual codebook storage unit  142 E. The class taps generated by the tap generator  143 A are supplied to the classification unit  144 A, while the prediction taps are sent to the prediction unit  146 A. The class taps generated by the tap generator  143 E are sent to the classification unit  144 E, while the prediction taps are sent to the prediction unit  146 E. 
     At step S 102 , the classification units  144 A,  144 E perform classification based on the class taps supplied from the tap generators  143 A,  143 E and sends the resulting class codes to the coefficient memories  145 A,  145 E. The program then moves to step S 103 . 
     At step S 103 , the coefficient memories  145 A,  145 E read out tap coefficients from the addresses for the class codes sent from the classification units  144 A,  144 E to send the read out coefficients to the prediction units  146 A,  146 E. 
     The program then moves to step S 104 , where the prediction unit  146 A acquires the tap coefficients output by the coefficient memory  145 A and, using these tap coefficients and the prediction taps from the tap generator  143 A, acquires the prediction values of the true linear prediction coefficients of the frame of interest. At step S 104 , the prediction unit  146 E acquires the tap coefficients output by the coefficient memory  145 E and, using the tap coefficients and the prediction taps from the tap generator  143 E, performs the sum-of-products processing shown by the equation (6) to acquire the true residual signals of the frame of interest, more precisely predicted values thereof. 
     The residual signals and the linear prediction coefficients, obtained as described above, are sent to the speech synthesis filter  147 , which then performs the calculations of the equation (4), using the residual signals and the linear prediction coefficients, to produce the synthesized sound signal of the frame of interest. The synthesized sound signal is sent from the speech synthesis filter  147  through the D/A converter  148  to the loudspeaker  149  which then outputs the synthesized sound corresponding to the synthesized sound signal. 
     After the linear prediction coefficients and the residual signals have been obtained in the prediction units  146 A,  146 E, the program moves to step S 105  where it is verified whether or not there are any decoded linear prediction coefficients and the decoded residual signals to be processed as the frame of interest. If it is verified at step S 105  that there are any decoded linear prediction coefficients and the decoded residual signals to be processed as the frame of interest, the program reverts to step S 101  where the frame to be rendered the frame of interest next is rendered the new frame of interest. The similar sequence of operations is then carried out. If it is verified at step S 105  that there are no decoded linear prediction coefficients nor decoded residual signals to be processed as the frame of interest, the speech synthesis processing is terminated. 
     The learning device for carrying out the tap coefficients to be stored in the coefficient memories  145 A,  145 E shown in  FIG. 14  is configured as shown in  FIG. 17 . 
     The learning device, shown in  FIG. 17 , is fed with the digital speech signals for learning, on the frame basis. These digital speech signals for learning are sent to an LPC analysis unit  161 A and to a prediction filter  161 E. 
     The LPC analysis unit  161 A sequentially renders the frames of the speech signals, supplied thereto, the frames of interest, and LPC-analyzes the speech signals of the frame of interest to find p-dimensional linear prediction coefficients. These linear prediction coefficients are sent to a prediction unit  161 E and to a vector quantizer  162 A, while being sent to a normal equation addition circuit  166 A as teacher data for finding tap coefficients pertinent to the linear prediction coefficients. 
     The prediction filter  161 E performs calculations in accordance with the equation (1), using the speech signals and the linear prediction coefficients, supplied thereto, to find residual signals of the frame of interest, to send the resulting signals to the vector quantizer  162 E, as well as to send the residual signals to the normal equation addition circuit  166 E as teacher data for finding tap coefficients pertinent to the linear prediction coefficients. 
     That is, if the Z-transforms of s n  and e n  in the equation (1) are represented by S and E, respectively the equation (1) may be represented by:
 
 E= (1+α 1   z   −1 +α 2   z   −2 + . . . +α p   z   −p ) S.   (15)
 
     From the equation (15), the residual signals e can be found by the sum-of-products processing of the speech signal s and the linear prediction coefficients α p , so that the prediction filter  161 E for finding the residual signals e may be formed by an FIR (Finite Impulse Response) digital filter. 
       FIG. 18  shows an illustrative structure of the prediction filter  161 E. 
     The prediction filter  161 E is fed with p-dimensional linear prediction coefficients from the LPC analysis unit  161 A. So, the prediction filter  161 E is made up of p delay circuits (D)  171   1  to  171   p , p multipliers  172   1  to  172   p  and one adder  173 . 
     In the multipliers  172   1  to  172   p  are set α 1 , α 2 , . . . , α p  from among the p-dimensional linear prediction coefficients sent from the LPC analysis unit  161 A. 
     The speech signals s of the frame of interest are sent to a delay circuit  171   1  and to an adder  173 . The delay circuit  171   p  delays the input signal thereto by one sample of the residual signals to output the delayed signal to the downstream side delay circuit  171   p+1  and to the multiplier  172   p . The multiplier  172   p  multiplies the output of the delay circuit  171   p  with the linear prediction coefficient α p  to send the resulting product to the adder  173 . 
     The adder  173  sums all of the outputs of the multipliers  172   1  to  172   p  to the speech signals s to output the results of summation as the residual signals e. 
     Returning to  FIG. 17 , the vector quantizer  162 A holds a codebook which associates the code vectors having the linear prediction coefficients as components with the codes. Based on the codebook, the vector quantizer  162 A vector-quantizes the feature vector constituted by linear prediction coefficients of the frame of interest from the LPC analysis unit  161 A to route the code A obtained on the vector quantization to a filter coefficient decoder  163 A. The vector quantizer  162 A holds a codebook, which associates the code vectors, having the sample values of the signal of the vector quantizer  162  as components, with the codes, and vector-quantizes the residual vectors, formed by sample values of the residual signals of the frame of interest from the prediction filter  161 E to route the residual code obtained on this vector quantization to a residual codebook storage unit  163 E. 
     The filter coefficient decoder  163 A holds the same codebook as that stored by the vector quantizer  162 A and, based on this codebook, decodes the A code from the vector quantizer  162 A into decoded linear prediction coefficients which then are sent to the tap generator  164 A as pupil data used for finding the tap coefficients pertinent to the linear prediction coefficients. The residual codebook storage unit  142 E shown in  FIG. 14  is configured similarly to the filter coefficient decoder  163 A shown in  FIG. 17 . 
     The residual codebook storage unit  163 E holds the same codebook as that stored by the vector quantizer  162 E and, based on this codebook, decodes the residual code from the vector quantizer  162 E into decoded residual signals which then are sent to the tap generator  164 E as pupil data used for finding the tap coefficients pertinent to the residual signals. The residual codebook storage unit  142 E shown in  FIG. 14  is configured similarly to the residual codebook storage unit  142 E shown in  FIG. 17 . 
     Similarly to the tap generator  143 A of  FIG. 14 , the tap generator  164 A forms prediction taps and class taps, from the decoded linear prediction coefficients, supplied from the filter coefficient decoder  163 A, to send the class taps to a classification unit  165 A, while supplying the prediction taps to the normal equation addition circuit  166 A. Similarly to the tap generator  143 E of  FIG. 14 , the tap generator  164 E forms prediction taps and class taps, from the decoded residual signals supplied from the residual codebook storage unit  163 E, to send the class taps and the prediction taps to the classification unit  165 E and to the normal equation addition circuit  166 E. 
     Similarly to the classification units  144 A and  144 E of  FIG. 3 , the classification units  165 A and  165 E perform classification based on the class taps supplied thereto to send the resulting class codes to the normal equation addition circuits  166 A and  166 E. 
     The normal equation addition circuit  166 A executes summation on the linear prediction coefficients of the frame of interest, as teacher data from the LPC analysis unit  161 A, and on the decoded linear prediction coefficients, forming prediction taps, as pupil data from the tap generator  164 A. The normal equation addition circuit  166 E executes summation on the residual signals of the frame of interest, as teacher data from the prediction filter  161 E, and on the decoded residual signals, forming prediction taps, as pupil data from the tap generator  164 E. 
     That is, the normal equation addition circuit  166 A uses the pupil data, as prediction taps and to perform calculations equivalent to the reciprocal multiplication of the pupil data (x in x im ), as the components of the matrix A of the above-mentioned equation (13), and to summation (Σ), for each class supplied from the classification unit  165 A. 
     The normal equation addition circuit  166 A also uses pupil data, that is linear prediction coefficients of the frame of interest, and teacher data, that is the decoded linear prediction coefficients, forming the prediction taps, and the linear prediction coefficients of the frame of interest, as teacher data, to perform multiplication (x in y i ) of the pupil and teacher data, and to summation (Σ), for each class of the class code supplied from the classification unit  165 A. 
     The normal equation addition circuit  166 A performs the aforementioned summation, with the totality of the frames of the linear prediction coefficients supplied from the LPC analysis unit  161 A as the frames of interest, to establish the normal equation pertinent to the linear prediction coefficients shown in  FIG. 13 . 
     The normal equation addition circuit  166 E also performs similar summation, with all of the frames of the residual signals sent form the prediction filter  161 E as the frame of interest, whereby a normal equation concerning the residual signals as shown in equation (13) is established for each class. 
     A tap coefficient decision circuit  167 A and a tap coefficient decision circuit  167 E solve the normal equations, generated in the normal equation addition circuits  166 A,  166 E, from class to class, to find tap coefficients for the linear prediction coefficients and for the residual signals, which are sent to addresses associated with respective classes of the coefficient memories  168 A,  168 E. 
     Depending on the speech signals, provided as speech signals for learning, there are occasions wherein, in a class or classes, a number of the normal equations required to find tap coefficients cannot be produced in the normal equation addition circuit  166 A or  166 E. For such class(es), the tap coefficient decision circuit  167 A or  167 E outputs default tap coefficients. 
     The coefficient memories  168 A,  168 E memorize the class-based tap coefficients and residual signals, supplied from the tap coefficient decision circuits  167 A,  167 E. 
     Referring to the flowchart of  FIG. 19 , the processing for learning of the learning device of  FIG. 17  is explained. 
     The learning device is supplied with speech signals for learning. At step S 111 , teacher data and pupil data are generated from the speech signals for learning. 
     That is, the LPC analysis unit  161 A sequentially renders the frames of the speech signals for learning, the frame of interest, and LPC-analyzes the speech signals of the frame of interest to find p-dimensional linear prediction coefficients, which are sent as teacher data to the normal equation addition circuit  166 A. These linear prediction coefficients are also sent to the prediction filter  161 E and to the vector quantizer  162 A. This vector quantizer  162 A vector-quantizes the feature vector formed by the linear prediction coefficients of the frame of interest from the LPC analysis unit  161 A to send the A code obtained by this vector quantization to the filter coefficient decoder  163 A. The filter coefficient decoder  163 A decodes the A code from the vector quantizer  162 A into decoded linear prediction coefficients which are sent as pupil data to the tap generator  164 A. 
     On the other hand, the prediction filter  161 E, which has received the linear prediction coefficients of the frame of interest from the analysis unit  161 A, performs the calculations conforming to the aforementioned equation (1), using the linear prediction coefficients and the speech signals for learning of the frame of interest, to find the residual signals of the frame of interest, which are sent to the normal equation addition circuit  166 E as teacher data. These residual signals are also sent to the vector quantizer  162 E. This vector quantizer  162 E vector-quantizes the residual vector, constituted by sample values of the residual signals of the frame of interest from the prediction filter  161 E to send the residual code obtained as the result of the vector quantization to the residual codebook storage unit  163 E. The residual codebook storage unit  163 E decodes the residual code from the vector quantizer  162 E to form decoded residual signals, which are sent as pupil data to the tap generator  164 E. 
     The program then moves to step S 112  where the tap generator  164 A forms prediction taps and class taps pertinent to the linear prediction coefficients, from the decoded linear prediction coefficients sent from the filter coefficient decoder  163 A, whilst the tap generator  164 E forms prediction taps and class taps pertinent to the residual signals from the decoded residual signals supplied from the residual codebook storage unit  163 E. The class taps pertinent to the linear prediction coefficients are sent to the classification unit  165 A, whilst the prediction taps are sen to the normal equation addition circuit  166 A. The class taps pertinent to the residual signals are sent to the classification unit  165 E, whilst the prediction taps are sen to the normal equation addition circuit  166 E. 
     Subsequently, at step S 113 , the classification unit  165 A executes classification based on the class taps pertinent to the linear prediction coefficients, and sends the resulting class codes to the normal equation addition circuit  166 A, whilst the classification unit  165 E executes classification based on the class taps pertinent to the residual signals, and sends the resulting class code to the normal equation addition circuit  166 E. 
     The program then moves to step S 114 , where the normal equation addition circuit  166 A performs the aforementioned summation of the matrix A and the vector v of the equation (13), for the linear prediction coefficients of the frame of interest as teacher data from the LPC analysis unit  161 A and for the decoded linear prediction coefficients forming the prediction taps as pupil data from the tap generator  164 A. At step S 114 , the normal equation addition circuit  166 E performs the aforementioned summation of the matrix A and the vector v of the equation (13), for the residual signals of the frame of interest as teacher data from the prediction filter  161 E and for the decoded residual signals forming the prediction taps as pupil data from the tap generator  164 E. The program then moves to step S 115 . 
     At step S 115 , it is verified whether or not there is any speech signal for learning for the frame to be processed as the frame of interest. If it is verified at step S 115  that there is any speech signal for learning of the frame to be processed as the frame of interest, the program reverts to step S 111  where the next frame is set as a new frame of interest. The processing similar to that described above then is repeated. 
     If it is verified at step S 105  that there is no speech signal for learning of the frame to be processed as the frame of interest, that is if the normal equation is obtained in each class in the normal equation addition circuits  166 A,  166 E, the program moves to step S 116  where the tap coefficient decision circuit  167 A solves the normal equation generated for each class to find the tap coefficients for the linear prediction coefficients for each class. These tap coefficients are sent to the address associated with each class for storage therein. The tap coefficient decision circuit  167 E also solves the normal equation generated for each class to find the tap coefficients for the residual signals for each class. These tap coefficients are sent to and stored in the address associated with each class to terminate the processing. 
     The tap coefficients pertinent to the linear prediction coefficients for each class, thus stored in the coefficient memory  168 A, are stored in the coefficient memory  145 A of  FIG. 14 , while the tap coefficients pertinent to the class-based residual signals stored in the coefficient memory  168 E are stored in the coefficient memory  145 E of  FIG. 14 . 
     Consequently, the tap coefficients stored in the coefficient memory  145 A of  FIG. 14  have been found on learning so that the prediction errors of the prediction value of the true linear prediction coefficients, obtained on carrying out linear predictive calculations, herein square errors, will be statistically minimum, while the tap coefficients stored in the coefficient memory  145 E of  FIG. 14  have been found on learning so that the prediction errors of the prediction values of the true residual signals, obtained on carrying out linear predictive calculations, herein square errors, will also be statistically minimum. Consequently, the linear prediction coefficients and the residual signals, output by the prediction units  146 A,  146 E of  FIG. 14 , are substantially coincident with the true linear prediction coefficients and with the true residual signals, respectively, with the result that the synthesized sound generated by these linear prediction coefficients and residual signals are free of distortion and of high sound quality. 
     If, in the speech synthesis device, shown in  FIG. 14 , the class taps and prediction taps for the linear prediction coefficients are to be extracted by the tap generator  143 A from both the decoded linear prediction coefficients and the decoded residual signals, it is necessary to cause the tap generator  164 A of  FIG. 17  to extract the class taps or prediction taps for the linear prediction coefficients from both the decoded linear prediction coefficients and from the decoded residual signals. The same holds for the tap generator  164 E. 
     If, in the speech synthesis device shown in  FIG. 14 , the tap generators  143 A,  143 E, classification units  144 A,  144 E and the coefficient memories  145 A,  145 E are constructed as respective separate units, the tap generators  164 A,  164 E, classification units  165 A,  165 E, normal equation addition circuits  166 A,  166 E, tap coefficient decision circuits  167 A,  167 E and the coefficient memories  168 A,  168 E need to be constructed as respective separate units. In this case, in the normal equation addition circuit in which the normal equation addition circuits  166 A,  166 E are constructed unitarily, the normal equation is established with both the linear predictive coefficients output by the LPC analysis unit  161 A and the residual signals output by the prediction units  161 E as teacher data at a time and with both the decoded linear predictive coefficients output by the filter coefficient decoder  163 A and the decoded residual signals output by the residual codebook storage unit  163 E as pupil data at a time. In the tap coefficient decision circuit where the tap coefficient decision circuits  167 A,  167 E are constructed unitarily, the normal equation is solved to find the tap coefficients for the linear predictive coefficients and for the residual signals for each class at a time. 
     An instance of the transmission system embodying the present invention the present invention is now explained with reference to  FIG. 20 . The system herein means a set of logically arrayed plural devices, while it does not matter whether or not the respective devices are in the same casing. 
     In this transmission system, the portable telephone sets  181   1 ,  181   2  perform radio transmission and receipt with base stations  182   1 ,  182   2 , respectively, while the base stations  182   1 ,  182   2  perform speech transmission and receipt with an exchange station  183  to enable speech transmission and receipt of speech between the portable telephone sets  181   1 ,  181   2  with the aid of the base stations  182   1 ,  182   2  and the exchange station  183 . The base stations  182   1 ,  182   2  may be the same as or different from each other. 
     The portable telephone sets  181   1 ,  181   2  are referred to below as a portable telephone set  181 , unless there is no particular necessity for making distinctions between the two sets. 
       FIG. 21  shows an illustrative structure of the portable telephone set  181  shown in  FIG. 20 . 
     An antenna  191  receives electrical waves from the base stations  182   1 ,  182   2  to send the received signals to a modem  192  as well as to send the signals from the modem  192  to the base stations  182   1 ,  182   2  as electrical waves. The modem  192  demodulates the signals from the antenna  191  to send the resulting code data explained in  FIG. 1  to a receipt unit  194 . The modem  192  also is configured for modulating the code data from the transmitter  193  as shown in  FIG. 1  and sends the resulting modulated signal to the antenna  191 . The transmission unit  193  is configured similarly to the transmission unit shown in  FIG. 1  and codes the user&#39;s speech input thereto into code data which is sent to the modem  192 . The receipt unit  194  receives the code data from the modem  192  to decode and output the speech of high sound quality similar to that obtained in the speech synthesis device of  FIG. 14 . 
     That is,  FIG. 22  shows an illustrative structure of the receipt unit  194  of  FIG. 21 . In the drawing, parts or components corresponding to those shown in  FIG. 2  are depicted by the same reference numerals and are not explained specifically. 
     The tap generator  101  is fed with frame-based or subframe-based L, G and A codes, output by a channel decoder  21 . The tap generator  101  generates what are to be class taps, from the L, G, I and A codes, to route the extracted class taps to a classification unit  104 . The class taps, constructed by e.g., records, generated by the tap generator  101 , are sometimes referred to below as first class taps. 
     The tap generator  102  is fed with frame-based or subframe-based residual signals e, output by the operating unit  28 . The tap generator  102  extracts what are to be class taps (sample points) from the residual signals to route the resulting class taps to the classification unit  104 . The tap generator  102  also extracts what are to be prediction taps from the residual signals from the operating unit  28  to route the resulting prediction taps to the classification unit  106 . The class taps, constructed by e.g., residual signals, generated by the tap generator  102 , are sometimes referred to below as second class taps. 
     The tap generator  103  is fed with frame-based or subframe-based linear prediction coefficients α 1 , output by the filter coefficient decoder  25 . The tap generator  103  extracts what are to be class taps from the linear prediction coefficients to route the resulting class taps to the classification unit  104 . The tap generator  103  also extracts what are to be prediction taps from the linear prediction coefficients from the filter coefficient decoder  25  to route the resulting prediction taps to the prediction unit  107 . The class taps, constructed by e.g., the linear prediction coefficients, generated by the tap generator  103 , are sometimes referred to below as third class taps. 
     The classification unit  104  integrates the first to third class taps, supplied from the tap generators  101  to  103 , to form ultimate class taps. Based on these ultimate class taps, the classification unit  104  performs the classification to send the class code as being the result of the classification to the coefficient memory  105 . 
     The coefficient memory  105  holds the tap coefficients pertinent to the class-based linear prediction coefficients and the tap coefficients pertinent to the residual signals, as obtained by the learning processing in the learning device of  FIG. 23 , as will be explained subsequently. The coefficient memory  105  outputs the tap coefficients stored in the address associated with the class code output by the classification unit  104  to the prediction units  106  and  107 . Meanwhile, tap coefficients We pertinent to the residual signals are sent from the coefficient memory  105  to the prediction unit  106 , while tap coefficients Wa pertinent to the linear prediction coefficients are sent from the coefficient memory  105  to the prediction unit  107 . 
     Similarly to the prediction unit  146 E, the prediction unit  106  acquires the prediction taps output by the tap generator  102  and the tap coefficients pertinent to the residual signals, output by the coefficient memory  105 , and performs the linear predictive calculations of the equation (6), using the prediction taps and the tap coefficients. In this manner, the prediction unit  106  finds a predicted value em of the residual signals of the frame of interest to send the predicted value em to the speech synthesis unit  29  as an input signal. 
     Similarly to the prediction unit  146 A of  FIG. 14 , the prediction unit  107  acquires the prediction taps output by the tap generator  103  and tap coefficients pertinent to the linear prediction coefficients output by the coefficient memory and, using the prediction taps and the tap coefficients, executes the linear predictive calculations of the equation (6). So, the prediction unit  107  finds a predicted value mα p  of the linear prediction coefficients of the frame of interest to send the so found out predicted value to the speech synthesis unit  29 . 
     In the receipt unit  194 , constructed as described above, the processing which is basically the same as the processing conforming to the flowchart of  FIG. 16  is carried out to output the synthesized speech of the high sound quality as being the result of the speech decoding. 
     That is, the channel decoder  21  separates the L, G, I and A codes, from the code data, supplied thereto, to send the so separated codes to the adaptive codebook storage unit  22 , gain decoder  23 , excitation codebook storage unit  24  and to the filter coefficient decoder  25 , respectively. The L, G, I and A codes are also sent to the tap generator  101 . 
     The adaptive codebook storage unit  22 , gain decoder  23 , excitation codebook storage unit  24  and the operating units  26  to  28  perform the processing similar to that performed in the adaptive codebook storage unit  9 , gain decoder  10 , excitation codebook storage unit  11  and in the operating units  12  to  14  of  FIG. 1  to decode the L, G and I codes to residual signals e. These residual signals are routed from the operating unit  28  and to the tap generator  102 . 
     As explained with reference to  FIG. 1 , the filter coefficient decoder  25  decodes the A codes, supplied thereto, into linear prediction coefficients, which are routed to the tap generator  103 . 
     The tap generator  101  renders the frames of the L, G, I and A codes, supplied thereto, the frame of interest. At step S 101  ( FIG. 16 ), the tap generator  101  generates first class taps from the L, G, I and A codes from the channel decoder  21  to send the so generated first class taps to the classification unit  104 . At step S 101 , the tap generator  102  generates second class taps from the decoded residual signals from the operating unit  28  to send the so generated second class taps to the classification unit  104 , while the tap generator  103  generates the third class taps from the linear prediction coefficients from the filter coefficient decoder  25  to send the so generated third class taps to the classification unit  104 . At step S 101 , the tap generator  102  generates what are to be prediction taps from the residual signals from the operating unit  28  to send the prediction taps to the prediction unit  106 , while the tap generator  102  generates prediction taps from the linear prediction coefficients from the filter coefficient decoder  25  to send the so generated prediction taps to the prediction unit  107 . 
     At step S 102 , the classification unit  104  executes classification based on ultimate class taps which have combined the first to third class taps supplied from the tap generators  101  to  103  and sends the resulting class codes to the coefficient memory  105 . The program then moves to step S 103 . 
     At step S 103 , the coefficient memory  105  reads out the tap coefficients concerning the residual signals and the linear prediction coefficients, from the address associated with the class code as supplied from the classification unit  104 , and sends the tap coefficients pertinent to the residual signals and the tap coefficients pertinent to the linear prediction coefficients to the prediction units  106 ,  107 , respectively. 
     At step S 104 , the prediction unit  106  acquires the tap coefficients concerning the residual signals, output from the coefficient memory  105 , and executes the sum-of-products processing of the equation (6), using the so acquired tap coefficients and the prediction taps from the tap generator  102 , to acquire predicted values of true residual signals of the frame of interest. At this step S 104 , the prediction unit  107  also acquires the tap coefficients pertinent to the linear prediction coefficients output by the prediction unit  105  and, using the so acquired tap coefficients and the tap coefficients from the tap generator  103 , performs the sum-of-products processing of the equation (6) to acquire predicted values of true linear prediction coefficients of the frame of interest. 
     The residual signals and the linear prediction coefficients, thus acquired, are routed to the speech synthesis unit  29 , which then performs the processing of the equation (4), using the residual signals and the linear prediction coefficients, to generate the synthesized sound signal of the frame of interest. These synthesized sound signals are sent from the speech synthesis unit  29  through the D/A converter  30  to the loudspeaker  31  which then outputs the synthesized sound corresponding to the synthesized sound signals. 
     After the residual signals and the linear prediction coefficients have been acquired by the prediction units  106 ,  107 , the program moves to step S 105  where it is verified whether or not there are yet L, G, I or A codes of the frame to be processed as the frame of interest. If it is found at step S 105  that there are as yet the L, G, I or A codes of the frame to be processed as the frame of interest, the program reverts to step S 101  to set the frame to be the next frame of interest as the new frame of interest to repeat the processing similar to that described above. If it is found at step S 105  that there are no L, G, I or A codes of the frame to be processed as the frame of interest, the processing is terminated. 
     An instance of a learning device for performing the learning processing of tap coefficients to be stored in the coefficient memory  105  shown in  FIG. 22  is now explained with reference to  FIG. 23 . In the following explanation, parts or components common to those of the learning device shown in  FIG. 12  are depicted by corresponding reference numerals. 
     The components from the microphone  201  to the code decision unit  215  are configured similarly to the components from the microphone  1  to the code decision unit  15 . The microphone  201  is fed with speech signals for learning, so that the components from the microphone  201  to the code decision unit  215  perform the processing similar to that shown in  FIG. 1 . 
     A prediction filter  111 E is fed with speech signals for learning, as digital signals, output by the A/D converter  202 , and with the linear prediction coefficients, output by the LPC analysis unit  204 . The tap generator  112 A is fed with the linear prediction coefficients, output by the vector quantizer  205 , that is linear prediction coefficients forming the code vectors (centroid vector) of the codebook used for vector quantization, while the tap generator  112 E is fed with residual signals output by the operating unit  214 , that is the same residual signals as those sent to the speech synthesis filter  206 . The normal equation addition circuit  114 A is fed with the linear prediction coefficients output by the LPC analysis unit  204 , whilst the tap generator  117  is fed with the L, G, I and A codes output by the code decision unit  215 . 
     The prediction filter  111 E sequentially sets the frames of the speech signals for learning, sent from the A/D converter  202 , and executes e.g., the processing complying with the equation (1), using the speech signals for the frame of interest and the linear prediction coefficients supplied from the LPC analysis unit  204 , to find the residual signals for the frame of interest. These residual signals are sent as teacher data to the normal equation addition circuit  114 E. 
     From the linear prediction coefficients, supplied from the vector quantizer  205 , the tap generator  112 A forms the same prediction taps as those in the tap generator  103  of  FIG. 11 , and third class taps, and routes the third class taps to the classification units  113 A,  113 E, while routing the prediction taps to the normal equation addition circuit  114 A. 
     From the linear prediction coefficients, supplied from the operating unit  214 , the tap generator  112 E forms the same prediction taps as those in the tap generator  102  of  FIG. 22 , and second class taps, and routes the second class taps to the classification units  113 A,  113 E, while routing the prediction taps to the normal equation addition circuit  114 E. 
     The classification units  113 A,  113 E are fed with the third and second class taps, from the tap generators  112 A,  112 E, respectively, while being fed with the first class taps from the tap generator  117 . Similarly to the classification unit  104  of  FIG. 22 , the classification units  113 A,  113 E integrate the first to third class taps, supplied thereto, to form ultimate class taps. Based on these ultimate class taps, the classification units perform the classification to send the class code to the normal equation addition circuits  114 A,  114 E. 
     The normal equation addition circuit  114 A receives the linear prediction coefficients of the frame of interest from the LPC analysis unit  204 , as teacher data, while receiving the prediction taps from the tap generator  112 A, as pupil data. The normal equation addition circuit performs the summation, as the normal equation addition circuit  166 A of  FIG. 17 , for the teacher data and the pupil data, from one class code from the classification unit  113 A to another, to set the normal equation (13) pertinent to the linear prediction coefficients, from one class to another. The normal equation addition circuit  114 E receives the residual signals of the frame of interest from the prediction unit  111 E, as teacher data, while receiving the prediction taps from the tap generator  112 E, as pupil data. The normal equation addition circuit performs the summation, as the normal equation addition circuit  166 E of  FIG. 17 , for the teacher data and the pupil data, from one class code from the classification unit  113 E to another, to set the normal equation (13) pertinent to the residual signals, from one class to another. A tap coefficient decision circuit  115 A and a tap coefficient decision circuit  115 E solve the normal equation, generated in the normal equation addition circuits  114 A,  114 E, from class to class, to find tap coefficients pertinent to the linear prediction coefficients and the residual signals for the respective classes. The tap coefficients, thus found, are sent to the addresses of the coefficient memories  116 A,  116 E associated with the respective classes. 
     Depending on the speech signals, provided as speech signals for learning, there are occasions wherein, in a class or classes, a number of the normal equations required to find the tap coefficients cannot be produced in the normal equation addition circuits  114 A,  114 E. For such class(es), the tap coefficient decision circuits  115 A,  115 E outputs e.g., default tap coefficients. 
     The coefficient memories  116 A,  116 E memorize the class-based tap coefficients pertinent to linear prediction coefficients and residual signals, supplied from the tap coefficient decision circuits  115 A,  115 E, respectively. 
     From the L, G, I and the A codes, supplied from the code decision unit  215 , the tap generator  117  generates the same first class taps as those in the tap generator  101  of  FIG. 22 , to send the so generated class taps to the classification units  113 A,  113 E. 
     The above-described learning device basically performs the same processing as the processing conforming to the flowchart of  FIG. 19  to find the tap coefficients necessary to produce the synthesized sound of high sound quality. 
     The learning device is fed with the speech signals for learning and generates teacher data and pupil data at step S 111  from the speech signals for learning. 
     That is, the speech signals for learning are input to the microphone  201 . The components from the microphone  201  to the code decision unit  215  perform the processing similar to that performed by the microphone  1  to the code decision unit  15  of  FIG. 1 . 
     The linear prediction coefficients, acquired by the LPC analysis unit  204 , are sent as teacher data to the normal equation addition circuit  114 A. These linear prediction coefficients are also sent to the prediction filter  111 E. The residual signals, obtained in the operating unit  214 , are sent as pupil data to the tap generator  112 E. 
     The digital speech signals, output by the A/D converter  202 , are sent to the prediction filter  111 E, while the linear prediction coefficients, output by the vector quantizer  205 , are sent as pupil data to the tap generator  112 A. The L, G, I and A codes, output by the code decision unit  215 , are sent to the tap generator  117 . 
     The prediction filter  111 E sequentially renders the frames of the speech signals for learning, supplied from the A/D converter  202 , the frame of interest, and executes the processing conforming to the equation (1), using the speech signals of the frame of interest and the linear prediction coefficients supplied from the LPC analysis unit  204 , to find the residual signals of the frame of interest. The residual signals, obtained by this prediction filter  111 E, are sent as teacher data to the normal equation addition circuit  114 E. 
     After acquisition of the teacher and pupil data as described above, the program moves to step S 112  where the tap generator  112 A generates prediction taps pertinent to linear prediction coefficients supplied from the vector quantizer  205 , and third class taps, from the linear prediction coefficients, while the tap generator  112 E generates the prediction taps pertinent to residual signals supplied from the operating unit  214 , and the second class taps, from the residual signals. Further, at step S 112 , the first class taps are generated by the tap generator  117  from the L, G, I and A codes supplied from the code decision unit  215 . 
     The prediction taps pertinent to the linear prediction coefficients are sent to the normal equation addition circuit  114 A, while the prediction taps pertinent to the residual signals are sent to the normal equation addition circuit  114 E. The first to third class taps are sent to the classification circuits  113 A,  113 E. 
     Subsequently, at step S 113 , the classification units  113 A,  113 E perform classification, based on the first to third class taps, to send the resulting class code to the normal equation addition circuits  114 A,  114 E. 
     The program then moves to step S 114 , where the normal equation addition circuit  114 A performs the aforementioned summation of the matrix A and the vector v of the equation (13), for the linear prediction coefficients of the frame of interest from the LPC analysis unit  204 , as teacher data, and for the prediction taps from the tap generator  112 A, as pupil data, for each class code from the classification unit  113 A. At step S 114 , the normal equation addition circuit  114 E performs the aforementioned summation of the matrix A and the vector v of the equation (13), for the residual signals of the frame of interest as teacher data from the prediction filter  111 E and for the prediction taps as pupil data from the tap generator  112 E, for each class code from the classification unit  113 E. The program then moves to step S 115 . 
     At step S 115 , it is verified whether or not there is any speech signal for learning for the frame to be processed as the frame of interest. If it is verified at step S 115  that there is any speech signal for learning of the frame to be processed as the frame of interest, the program reverts to step S 111  where the next frame is set as a new frame of interest. The processing similar to that described above then is repeated. 
     If it is verified at step S 115  that there is no speech signal for learning of the frame to be processed as the frame of interest, that is if the normal equation is obtained in each class in the normal equation addition circuits  114 A,  114 E, the program moves to step S 116  where the tap coefficient decision circuit  115 A solves the normal equation generated for each class to find the tap coefficients for the linear prediction coefficients for each class. These tap coefficients are sent to the address associated with each class of the coefficient memory  116 A for storage therein. The tap coefficient decision circuit  115 E solves the normal equation generated for each class to find the tap coefficients for the residual signals for each class. These tap coefficients are sent to the address associated with each class of the coefficient memory  116 E for storage therein. This finishes the processing. 
     The tap coefficients pertinent to the linear prediction coefficients for each class, thus stored in the coefficient memory  116 A, are stored in the coefficient memory  105  of  FIG. 22 , while the tap coefficients pertinent to the class-based residual signals stored in the coefficient memory  116 E are stored in the same coefficient memory. 
     Consequently, the tap coefficients stored in the coefficient memory  105  of  FIG. 22  have been found on learning so that the prediction errors of the prediction values of the true linear prediction coefficients or residual signals, obtained on carrying out linear predictive calculations, herein square errors, will be statistically minimum, and hence the residual signals and the linear prediction coefficients, output by the prediction units  106 ,  107  of  FIG. 22 , are substantially coincident with the true residual signals and with the true linear prediction coefficients, respectively, with the result that the synthesized sound generated by these residual signals and the linear prediction coefficients are free of distortion and of high sound quality. 
     The above-described sequence of operations may be carried out by hardware or by software. If the sequence of operations is carried out by software, the program forming the software is installed on e.g., a general-purpose computer. 
     The computer on which is installed the program for executing the above-described sequence of operations is configured as shown in  FIG. 13  as described above and the operation similar to that performed by the computer shown in  FIG. 13  is executed, and hence is not explained specifically for simplicity. 
     Referring to the drawings, a further modification of the present invention is hereinafter explained. 
     The speech synthesis device is fed with code data multiplexed from the residual code and the A code encoded e.g., on vector quantization from the residual signals and the linear prediction coefficients applied to a speech synthesis filter  244 . From the residual code and the A code, the residual signals and the linear prediction coefficients are decoded and sent to the speech synthesis filter  244  to generate the synthesized sound. The present speech synthesis device is designed to perform predictive processing, using the synthesized sound synthesized by the speech synthesis filter and the tap coefficients as found on learning to find and output the speech of high sound quality (synthesized sound) which is the synthesized sound improved in sound quality. 
     That is, the speech synthesis device, shown in  FIG. 24 , exploits the classification adaptive processing to decode the synthesized sound into predicted values of the true speech of high sound quality. 
     The classification adaptive processing is comprised of the classification processing and the adaptive processing. By the classification processing, data are classified according to properties and subjected to adaptive processing from class to class. The adaptive processing is carried out in the manner as described above and hence reference may be made to the previous description to omit the detailed description here for simplicity. 
     The speech synthesis device, shown in  FIG. 24 , decodes the decoded linear prediction coefficients to true linear prediction coefficients, more precisely predicted values thereof, by the above-described classification adaptive processing, while decoding the decoded residual signals to true residual signals, more precisely predicted values thereof. 
     That is, a demultiplexer (DEMUX)  241  is fed with code data and separates the frame-based A code and residual code from the code data supplied thereto. The demultiplexer  241  sends the A code to a filter coefficient decoder  242  and to tap generators  245 ,  246  to send the residual code to a residual codebook storage unit  243  and to tap generators  245 ,  246 . 
     It should be noted that the A code and the residual code, contained in the code data of  FIG. 24 , are obtained on vector quantization of the linear prediction coefficients and the residual signals, both obtained on LPC analyzing the speech, using a preset codebook. 
     The filter coefficient decoder  242  decodes the frame-based A code, supplied from the demultiplexer  241 , into linear prediction coefficients, based on the same codebook as that used in producing the A code, to send the so decoded linear prediction coefficients to the speech synthesis filter  244 . 
     The residual codebook storage unit  243  decodes the frame-based residual code, supplied from the demultiplexer  241 , based on the same codebook as that used in obtaining the residual code, to send the resulting residual signals to the speech synthesis filter  244 . 
     Similarly to the speech synthesis filter  29 , shown in  FIG. 2 , the speech synthesis filter  244  is an IIR type digital filter, and filters the residual signals from the residual codebook storage unit  243 , as an input signal, with the linear prediction coefficients from the filter coefficient decoder  242  as tap coefficients of the IIR filter, to generate the synthesized sound, which is sent to the tap generators  245 ,  246 . 
     The tap generator  245  extracts, from the sample values of the synthesized sound sent from the speech synthesis filter  244 , and from the residual code and the code A, supplied from the demultiplexer  241 , what are to be prediction taps used in predictive calculations in a prediction unit  249  as later explained. That is, the tap generator  245  sets the A code, residual code and the sample values of the synthesized sound of the frame of interest, for which predicted values of the high sound quality speech, for example, are to be found, as the prediction taps. The tap generator  245  routes the prediction taps to the prediction unit  249 . 
     The tap generator  246  extracts what are to be class taps from the sample values of the synthesized sound supplied from the speech synthesis filter  244 , and from the frame- or subframe-based A code and the residual code supplied from the demultiplexer  241 . Similarly to the tap generator  245 , the tap generator  246  sets all of the sample values of the synthesized sound of the frame of interest, the A code and the residual code, as the class taps. The tap generator  246  sends the class taps to a classification unit  247 . 
     The pattern of configuration of the prediction and class taps is not to be limited to the above-mentioned pattern. Although the class and prediction taps are the same in the above case, the class taps and the prediction taps may be different in configuration from each other. 
     In the tap generator  245  or  246 , the class taps and the prediction taps can also be extracted from the linear prediction coefficients, obtained from the A code, output from the filter coefficient decoder  242 , or from the residual signals obtained from the residual codes, output from the residual codebook storage unit  243 , as indicated by dotted lines in  FIG. 24 . 
     Based on the class taps from the tap generator  246 , the classification unit  247  classifies the speech sample values of the frame of interest, and outputs the class code, corresponding to the resulting class, to a coefficient memory  248 . 
     It is also possible for the classification unit  247  to output the bit strings per se, forming the sample values of the synthesized sound of the frame of interest, as class taps, the A code and the residual code. 
     The coefficient memory  248  holds class-based tap coefficients, obtained on learning in the learning device of  FIG. 27 , as later explained, and outputs to the prediction unit  249  the tap coefficients stored in the address corresponding to the class code output by the classification unit  247 . 
     If N samples of the speech of the high sound quality may be found for each frame, N sets of tap coefficients are needed to obtain N samples of the speech by the predictive calculations of the equation (6) for the frame of interest. Thus, in the present case, n sets of the tap coefficients are stored in the address of the coefficient memory  248  associated with one class code. 
     The prediction unit  249  acquires the prediction taps output by the tap generator  245  and the tap coefficients output by the coefficient memory  248  and performs linear predictive calculations as indicated by the equation (6) to find predicted values of the speech of the high sound quality of the frame of interest to output the resulting predicted values to a D/A converter  250 . 
     The coefficient memory  248  outputs N sets of tap coefficients for finding each of N samples of the speech of the frame of interest, as described above. The prediction unit  249  executes the sum-of-products processing of the equation (6), using the prediction taps for respective sample values and a set of tap coefficients associated with the respective sample values. 
     The D/A converter  250  D/A converts the prediction values of the speech from the prediction unit  249  from digital signals into analog signals, which are sent to and output at the loudspeaker  51 . 
       FIG. 25  shows a specified structure of the speech synthesis filter  244  shown in  FIG. 24 . The speech synthesis filter  244 , shown in  FIG. 25 , uses p-dimensional linear prediction coefficients, and hence is formed by an adder  261 , p delay circuits (D)  262   1  to  262   p  and p multipliers  263   1  to  263   p . 
     In the multipliers  263   1  to  263   p  are set p-dimensional linear prediction coefficients α 1 , α 2 , . . . , α p , supplied from the filter coefficient decoder  242 , so that the speech synthesis filter  244  performs the calculations conforming to the equation (4) to generate the synthesized sound. 
     That is, the residual signals e, output by the residual codebook storage unit  243 , are sent through an adder  261  to a delay circuit  262   1 . The delay circuit  262   p  delays the input signal thereto by one sample of the residual signals to output the resulting delayed signal to a downstream side delay circuit  262   p+1  and to an operating unit  263   p . The multiplier  263   p  multiplies an output of the delay circuit  262   p  with the linear prediction coefficient α p  set thereat to output the product value to the adder  261 . 
     The adder  261  sums all outputs of the multipliers  263   1  to  263   p  and the residual signals e to send the resulting sum to a delay circuit  262   1  as well as to output the result of speech synthesis (synthesized sound). 
     Referring to the flowchart of  FIG. 26 , the speech synthesis processing of the speech synthesis device of  FIG. 24  is explained. 
     The demultiplexer  241  sequentially separates the A code and the residual code, from the code data supplied thereto, on the frame basis, to send the respective codes to the filter coefficient decoder  242  and to the residual codebook storage unit  243 . The demultiplexer  241  also sends the A code and the residual code to the tap generators  245 ,  246 . 
     The filter coefficient decoder  242  sequentially decodes the frame-based A code, supplied from the demultiplexer  241 , into linear prediction coefficients, which are then sent to the speech synthesis filter  244 . The residual codebook storage unit  243  sequentially decodes the frame-based residual code, supplied from the demultiplexer  241 , into residual signals, which are then sent to the speech synthesis filter  244 . 
     The speech synthesis filter  244  then performs the calculations of the equation (4), using the residual signals and the linear prediction coefficients, supplied thereto, to generate the synthesized sound of the frame of interest. This synthesized sound is sent to the tap generators  245 ,  246 . 
     The tap generator  245  sequentially renders the frame of the synthesized sound, supplied thereto, the frame of interest. At step S 201 , the tap generator  245  generates prediction taps, from the sample values of the synthesized sound supplied from the speech synthesis filter  244  and from the A code and the residual code, supplied from the demultiplexer  241 , to output the so generated prediction taps to the prediction unit  249 . At step S 201 , the tap generator  246  generates class taps, from the synthesized sound sent from the speech synthesis filter  244  and from the A code and the residual code, supplied from the demultiplexer  241 , to route the so generated class taps to the classification unit  247 . 
     At step S 202 , the classification unit  247  executes the classification, based on the class taps supplied from the tap generator  246 , to send the resulting class code to the coefficient memory  248 . The program then moves to step S 203 . 
     At step S 203 , the coefficient memory  248  reads out the tap coefficients from the address associated with the class code sent from the classification unit  247  to send the so read out tap coefficients to the prediction unit  249 . 
     At step S 204 , the prediction unit  249  acquires the tap coefficients output by the coefficient memory  248  and, using the tap coefficients and the prediction taps from the tap generator  245 , executes the sum-of-products processing of the equation (6) to acquire predicted values of the speech of high sound quality of the frame of interest. The speech of the high sound quality is sent to and output at the loudspeaker  251  from the prediction unit  249  through the D/A converter  250 . 
     After the speech of the high sound quality is obtained at the prediction unit  249 , the program moves to step S 205  where it is verified whether or not there is any frame to be processed as the frame of interest. If it is verified at step S 205  that there is any frame to be processed as the frame of interest, the program reverts to step S 201  where a frame which is to become the next frame of interest is set as a new frame of interest. The similar processing is then repeated. If it is verified at step S 205  that there is no frame to be processed, the speech synthesis processing is terminated. 
       FIG. 27  is a block diagram showing an instance of a learning device adapted for performing the learning of the tap coefficients to be stored in the coefficient memory  248  shown in  FIG. 24 . 
     The learning device shown in  FIG. 27  is fed with digital speech signals for learning of high sound quality, in terms of a preset frame as a unit. The digital speech signals for learning are sent to an LPC analysis unit  271  and to a prediction filter  274 . The digital speech signals for learning are also sent as teacher data to a normal equation addition circuit  281 . 
     The LPC analysis unit  271  sequentially renders the frames of the speech signals, sent thereto, the frame of interest, and LPC-analyzes the speech signals of the frame of interest to find p-dimensional linear prediction coefficients, which then are sent to a vector quantizer  272  and to the prediction unit  274 . 
     The vector quantizer  272  holds a codebook which associates code vectors having the linear prediction coefficients as the code vectors with the codes and, based on this codebook, vector-quantizes the feature vector formed by linear prediction coefficients of the frame of interest from the LPC analysis unit  271  to send the A code resulting from the vector quantization to the filter coefficient decoder  273  and to tap generators  278 ,  279 . 
     The filter coefficient decoder  273  holds the same codebook as that stored in a vector quantizer  272  and, based on this codebook, decodes the A code from the vector quantizer  272  into linear prediction coefficients, which are sent to a speech synthesis filter  277 . It should be noted that the filter coefficient decoder  242  of  FIG. 24  is of the same structure as the filter coefficient decoder  273  of  FIG. 27 . 
     The prediction filter  274  performs the calculations conforming to the equation (1), using the speech signals of the frame of interest, supplied thereto, and the linear prediction coefficients from the LPC analysis unit  271 , to find the residual signals of the frame of interest, which are routed to a vector quantizer  275 . 
     That is, if the Z-transforms of s n  and e n  in the equation (1) are represented by S and E, respectively the equation (1) may be represented by:
 
 E= (1+α 1   z   −1 +α 2   z   −2 + . . . +α p   z   −p ) S.   (16)
 
     From the equation (14), the prediction filter  274  for finding the residual signals e may be designed as an FIR (Finite Impulse Response) digital filter. 
       FIG. 28  shows an illustrative structure of the prediction filter  274 . 
     The prediction filter  274  is fed with p-dimensional linear prediction coefficients from the LPC analysis unit  271 . So, the prediction filter  274  is made up of p delay circuits (D)  291   1  to  291   p , p multipliers  292   1  to  292   p  and a sole adder  293 . 
     In the multipliers  292   1  to  292   p , there are set p-dimensional linear prediction coefficients α 1 , α 2 , . . . , α p  supplied from the LPC analysis unit  271 . 
     On the other hand, the speech signals s of the frame of interest are sent to a delay circuit  291   1  and to an adder  293 . The delay circuit  291   p  delays the input signal thereat by one sample of the residual signals to output the delayed signal to a downstream side delay circuit  291   p+1  and to an operating unit  292   p . The multiplier  292   p  multiplies the output of the delay circuit  291   p  with the linear prediction coefficient α p  set thereat to send the result of addition as the residual signals e to the adder  293 . 
     The adder  293  sums all outputs of the multipliers  292   1  to  292   p  and the speech signals s to send the results of addition as the residual signals e. 
     Referring to  FIG. 27 , the vector quantizer  275  holds a codebook which associates code vectors with sample values of the residual signals as components and, based on this codebook, vector-quantizes the residual vector, constituted by sample values of the residual signals e of the frame of interest from the prediction filter  274  to send the residual code resulting from the vector quantization to the residual codebook storage unit  276  and to the tap generators  278 ,  279 . 
     The residual codebook storage unit  276  holds the same codebook as that stored in the vector quantizer  275  and, based on this codebook, decodes the residual code from the vector quantizer  275  into residual signals which are sent to the speech synthesis filter  277 . It should be noted that the stored contents of the residual codebook storage unit  243  of  FIG. 24  are the same as the stored contents of the residual codebook storage unit  276  of  FIG. 27 . 
     The speech synthesis filter  277  is an IIR type digital filter, constructed similarly to the speech synthesis filter  244  of  FIG. 24  and filters the residual signals from the filter residual codebook storage unit  276 , as an input signal, with the linear prediction coefficients from the filter coefficient decoder  273  as tap coefficients of the IIR filter, to generate the synthesized sound, which is sent to the tap generators  278 ,  279 . 
     Similarly to the tap generator  245  of  FIG. 24 , the tap generator  278  forms prediction taps from the synthesized sound from the speech synthesis filter  277 , the A code supplied from the vector quantizer  272  and from the residual code supplied from the vector quantizer  275  to send the so formed prediction taps to the normal equation addition circuit  281 . Also, the tap generator  279 , similarly to the tap generator  246  in  FIG. 24 , forms class taps from the synthesized sound from the speech synthesis filter  277 , the A code supplied from the vector quantizer  272  and from the residual code supplied from the vector quantizer  275  to send the so formed class taps to the normal equation addition circuit  280 . 
     Similarly to the classification unit  247  of  FIG. 24 , the classification unit  280  performs classification based on the class taps, supplied thereto, to send the resulting class code to the normal equation addition circuit  281 . 
     The normal equation addition circuit  281  executes summation of the speech for learning, which is the speech of high sound quality of the frame of interest, as teacher data, and prediction taps from the tap generator  78 , as pupil data. 
     That is, the normal equation addition circuit  281  performs calculations corresponding to reciprocal multiplication (x in x im ) and summation (Σ) of pupil data, as respective components in the aforementioned matrix A of the equation (13), using the prediction taps (pupil data), from one class corresponding to the class code supplied from the classification unit  280  to another. 
     Moreover, the normal equation addition circuit  281  performs calculations corresponding to reciprocal multiplication (x in y i ) and summation (Σ) of pupil data and teacher data, as respective components in the vector v of the equation (13), using the pupil data and the teacher data, from one class corresponding to the class code supplied from the classification unit  280  to another. 
     The aforementioned summation by the normal equation addition circuit  281  is carried out with the totality of the speech frames for learning, supplied thereto, to set a normal equation (13) for each class. 
     A tap coefficient decision circuit  281  solves the normal equation, generated in the normal equation addition circuit  281 , from class to class, to find tap coefficients pertinent to the linear prediction coefficients and the residual signals for the respective classes. The tap coefficients, thus found, are sent to the addresses of the coefficient memory  283  associated with the respective classes. 
     Depending on the speech signals, provided as speech signals for learning, there are occasions wherein, in a certain class or classes, a number of the normal equations required to find the tap coefficients cannot be produced in the normal equation addition circuit  281 . For such class(es), the tap coefficient decision circuit outputs e.g., default tap coefficients. 
     The coefficient memory  283  memorizes the class-based tap coefficients supplied from the tap coefficient decision circuit  281  in an address associated with the class. 
     Referring to the flowchart of  FIG. 29 , the learning processing of the learning device of  FIG. 27  is explained. 
     The learning device is fed with speech signals for learning. The speech signals for learning are sent to the LPC analysis unit  271  and to the prediction filter  274 , while being sent as teacher data to the normal equation addition circuit  281 . At step S 211 , pupil data are generated from the speech signals for learning, as teacher data. 
     Specifically, the LPC analysis unit  271  sequentially sets the frames of the speech signals for learning as the frame of interest and LPC-analyzes the speech signals of the frame of interest to find p-dimensional linear prediction coefficients which are sent to the vector quantizer  272 . The vector quantizer  272  vector-quantizes the feature vector formed by linear prediction coefficients of the frame of interest from the LPC analysis unit  271  to send the A code obtained on such vector quantization as pupil data to the filter coefficient decoder  273  and to the tap generators  278 ,  279 . The filter coefficient decoder  273  decodes the A code from the vector quantizer  272  into linear prediction coefficients, which then are routed to the speech synthesis filter  277 . 
     On receipt of the linear prediction coefficients of the frame of interest from the LPC analysis unit  271 , the prediction filter  274  executes the calculations of the equation (1), using the linear prediction coefficients and the speech signals for learning of the frame of interest, to find the residual signals of the frame of interest, which are then routed to the vector quantizer  275 . The vector quantizer  275  vector-quantizes the residual vector, formed by sample values of the residual signals of the frame of interest from the prediction filter  274 , and routes the residual code obtained on vector quantization as pupil data to the residual codebook storage unit  276  and to the tap generators  278 ,  279 . The residual codebook storage unit  276  decodes the residual code from the vector quantizer  275  into residual signals which are supplied to the speech synthesis filter  277 . 
     Thus, on receipt of the linear prediction coefficients and the residual signals, the speech synthesis filter  277  synthesizes the speech, using the linear prediction coefficients and the residual signals, and sends the resulting synthesized sound as pupil data to the tap generators  278 ,  279 . 
     The program then moves to step S 212  where the tap generator  278  generates prediction taps and class taps from the synthesized sound supplied from the speech synthesis filter  277 , A code supplied from the vector quantizer  272  and from the residual code supplied from the vector quantizer  275 . The prediction taps and the class taps are sent to the normal equation addition circuit  281  and to the classification unit  280 , respectively. 
     Subsequently, at step S 213 , the classification unit  280  performs classification, based on the class taps from the tap generator  279 , to send the resulting class code to the normal equation addition circuit  281 . 
     The program then moves to step S 214 , where the normal equation addition circuit  281  performs the aforementioned summation of the matrix A and the vector v of the equation (13), for the sample values of the speech of high sound quality of the frame of interest, supplied thereto, as teacher data, and for the prediction taps from the tap generator  278 , as pupil data, for each class code from the classification unit  280 . 
     The program then moves to step S 215 . 
     At step S 215 , it is verified whether or not there is any speech signal for learning for the frame processed as the frame of interest. If it is verified at step S 215  that there is any speech signal for learning of the frame processed as the frame of interest, the program reverts to step S 211  where the next frame is set as a new frame of interest. The processing similar to that described above then is repeated. 
     If it is verified at step S 215  that there is no speech signal for learning of the frame to be processed as the frame of interest, that is if the normal equation is obtained in each class in the normal equation addition circuit  281 , the program moves to step S 216  where the tap coefficient decision circuit  281  solves the normal equation generated for each class to find the tap coefficients for each class. These tap coefficients are sent to the address associated with each class of the coefficient memory  283  for storage therein. This finishes the processing. 
     The class-based tap coefficients, thus stored in the coefficient memory  283 , are stored in the coefficient memory  248  of  FIG. 24 . 
     Consequently, the tap coefficients stored in the coefficient memory  248  of  FIG. 3  have been found on learning so that the prediction errors of the prediction values of the true speech of high sound quality, obtained on carrying out linear predictive calculations, herein square errors, will be statistically minimum, so that the residual signals and the linear prediction coefficients, output by the prediction unit  249  of  FIG. 24 , are free of distortion proper to the synthesized sound produced in the speech synthesis filter  244  and hence of high sound quality. 
     If, in the tap generator  246  in the speech synthesis device, shown in  FIG. 24 , the class taps are to be extracted from the linear prediction coefficients and the residual signals, it is necessary for the tap generator  278  of  FIG. 27  to extract similar class taps from the linear prediction coefficients generated by the filter coefficient decoder  273  or from the residual signals output by the residual codebook storage unit  276 , as shown with dotted lines. The same holds for the prediction taps generated by the tap generator  245  of  FIG. 24  or by the tap generator  278  of  FIG. 27 . 
     For simplifying the explanation in the above case, the classification is carried out as the bit string forming the class tap is directly used as the class code. In this case, however, the number of the classes may be of an exorbitant value. Thus, in the classification, the class taps may be compressed by e.g., vector quantization to use the bit string resulting from the compression as the class code. 
     An instance of the transmission system embodying the present invention is now explained with reference to  FIG. 30 . The system herein means a set of logically arrayed plural devices, while it does not matter whether or not the respective devices are in the same casing. 
     In this transmission system, the portable telephone sets  401   1 ,  401   2  perform radio transmission and receipt with base stations  402   1 ,  402   2 , respectively, while the base stations  402   1 ,  402   2  perform speech transmission and receipt with an exchange station  403  to enable speech transmission and receipt between the portable telephone sets  401   1 ,  401   2  with the aid of the base stations  402   1 ,  402   2  and the exchange station  403 . The base stations  402   1 ,  402   2  may be the same as or different from each other. 
     The portable telephone sets  401   1 ,  401   2  are referred to below as a portable telephone set  401 , unless there is no particular necessity for making distinctions between the two sets. 
       FIG. 31  shows an illustrative structure of the portable telephone set  401  shown in  FIG. 30 . 
     An antenna  411  receives electrical waves from the base stations  402   1 ,  402   2  to send the received signals to a modem  412  as well as to send the signals from the modem  412  to the base stations  402   1 ,  402   2  as electrical waves. The modem  412  demodulates the signals from the antenna  411  to send the resulting code data explained in  FIG. 1  to a receipt unit  414 . The modem  412  also is configured for modulating the code data from the transmitter  413  as shown in  FIG. 1  and sends the resulting modulated signal to the antenna  411 . The transmission unit  413  is configured similarly to the transmission unit shown in  FIG. 1  and codes the user&#39;s speech input thereto into code data which is sent to the modem  412 . The receipt unit  414  receives the code data from the modem  412  to decode and output the speech of high sound quality similar to that obtained in the speech synthesis device of  FIG. 24 . 
     That is,  FIG. 32  shows an illustrative structure of the receipt unit  114  of the portable telephone set  401  shown in  FIG. 31 . In the drawing, parts or components corresponding to those shown in  FIG. 2  are depicted by the same reference numerals and are not explained specifically. 
     The frame-based synthesized sound, output by the speech synthesis unit  29 , and the frame-based or subframe-based L, G, I and A codes, output by a channel decoder  21  are sent to tap generators  221 ,  222 . The tap generators  221 ,  222  extract what are to be the prediction taps and what are to be class taps from the synthesized sound, L code, G code, I code and the A code, supplied thereto. The prediction taps are sent to a prediction unit  225 , while the class taps are sent to the classification unit  223 . 
     The classification unit  223  performs classification based on the class taps supplied from the tap generator  122  to route the class codes resulting from the classification to a coefficient memory  224 . 
     The coefficient memory  224  holds the class-based tap coefficients, obtained on learning by the learning device of  FIG. 33 , which will be explained subsequently. The coefficient memory sends the tap coefficients stored in the address associated with the class code output by the classification unit  223  to the prediction unit  225 . 
     Similarly to the prediction unit  249  of  FIG. 24 , the prediction unit  225  acquires the prediction taps output by the tap generator  221  and the tap coefficients output by the coefficient memory  224  and, using the prediction and class taps, performs the linear predictive calculations shown in equation (6). In this manner, the prediction unit  225  finds the predicted values of the speech of high sound quality of the frame of interest to route the so found out predicted values to the D/A converter  30 . 
     The receipt unit  414 , constructed as described above, performs the processing which is basically in meeting with the flowchart of  FIG. 26  to provide an output synthesized sound of high sound quality as being the result of speech decoding. 
     That is, the channel decoder  21  separates the L, G, I and A codes, from the code data, supplied thereto, to send the so separated codes to the adaptive codebook storage unit  22 , gain decoder  23 , excitation codebook storage unit  24  and to the filter coefficient decoder  25 , respectively. The L, G, I and A codes are also sent to the tap generators  221 ,  222 . 
     The adaptive codebook storage unit  22 , gain decoder  23 , excitation codebook storage unit  24  and the operating units  26  to  28  perform the processing similar to that performed in the adaptive codebook storage unit  9 , gain decoder  10 , excitation codebook storage unit  11  and in the operating units  12  to  14  of  FIG. 1  to decode the L, G and I codes to residual signals e. These residual signals are routed to the speech synthesis unit  29 . 
     As explained with reference to  FIG. 1 , the filter coefficient decoder  25  decodes the A codes, supplied thereto, into linear prediction coefficients, which are routed to speech synthesis unit  29 . The speech synthesis unit  29  performs speech synthesis, using the linear prediction coefficients from the filter coefficient decoder  25 , to send the resulting synthesized sound to the tap generators  221 ,  222 . 
     The tap generator  221  renders the frames of the synthesized sound output from the speech synthesis unit  29  a frame of interest. At step S 201 , the tap generator generates prediction taps from the synthesized sound of the frame of interest, and from the L, G, I and A codes, to route the so generated prediction taps to the prediction unit  225 . At step S 201 , the tap generator  222  generates class taps from the synthesized sound of the frame of interest and from the L, G, I and A codes to send the so generated class taps to the classification unit  223 . 
     At step S 202 , the classification unit  223  executes classification based on the class taps supplied from the tap generator  222  to send the resulting class code to the coefficient memory  224 . The program then moves to step S 203 . 
     At step S 203 , the coefficient memory  224  reads out tap coefficients from the address associated with the class code supplied from the classification unit  223  to send the read-out tap coefficients to the prediction unit  225 . 
     At step S 204 , the prediction unit  225  acquires the tap coefficients output by the coefficient memory  224  and, using the tap coefficients and the prediction taps from the tap generator  221 , executes the sum-of-products processing shown in equation (6) to acquire the predicted value of the speech of high sound quality of the frame of interest. 
     The speech of the high sound quality, obtained as described above, is sent from the prediction unit  225  through the D/A converter  30  to the loudspeaker  31  which then outputs the speech of high sound quality. 
     After the processing of step S 204 , the program moves to step S 205  where it is verified whether or not there is any frame to be processed as a frame of interest. If it is found that there is such frame, the program reverts to step S 201  where the frame which is to be the next frame of interest is set as the new frame of interest and subsequently the similar sequence of operations is repeated. If it is found at step S 205  that there is no frame to be processed as the frame of interest, the processing is terminated. 
     Referring to  FIG. 33 , an instance of a learning device for learning the tap coefficients to be stored in the coefficient memory  224  of  FIG. 32  is explained. 
     The components from a microphone  501  to a code decision unit  515  are configured similarly to the microphone  1  to the code decision unit  15  of  FIG. 1 . The microphone  501  is fed with speech signals for learning so that the components microphone  501  to the code decision unit  515  process the speech signals for learning as in the case of  FIG. 1 . 
     The synthesized sound output by a speech synthesis filter  506  when the square error is verified to be the smallest in a minimum square error decision unit  508   i  sent to tap generators  431 ,  432 . The tap generators  431 ,  432  are also fed with the L, G, I and A codes output when the code decision unit  515  has received the definite signal from the minimum square error decision unit  508 . The speech output by an A/D converter  202  is fed as teacher data to a normal equation addition circuit  434 . 
     A tap generator  431  forms the same prediction tap as that of the tap generator  221  of  FIG. 32 , based on the synthesized sound output by the speech synthesis filter  506  and the L, G, I and A codes output by the code decision unit  515 , to send the so formed prediction taps as pupil data to the normal equation addition circuit  234 . 
     A tap generator  232  also forms the same class taps as those of the tap generator  222  of  FIG. 32 , from the synthesized sound output by a speech synthesis filter  506  and the L, G, I and A codes output by the code decision unit  515 , and routes the so formed class taps to a classification unit  433 . 
     Based on the class taps from the tap generator  432 , the classification unit  433  performs classification in the same way as the classification unit  223  of  FIG. 32  to send the resulting class code to the normal equation addition circuit  434 . 
     The normal equation addition circuit  434  receives the speech from an A/D converter  502  as teacher data and prediction taps from the tap generator  131 . The normal equation addition circuit then performs summation as in the normal equation addition circuit  281  of  FIG. 27  to set a normal equation shown in the equation (13) for each class from the classification unit  433 . 
     A tap coefficient decision circuit  435  solves the normal equation, generated on the class basis, by the normal equation addition circuit  434 , to find tap coefficients from class to class, to send the so found tap coefficients to the address associated with each class of the coefficient memory  436 . 
     Depending on the speech signals, provided as speech signals for learning, there are occasions wherein, in a certain class or classes, a number of the normal equations required to find the tap coefficients cannot be produced in the normal equation addition circuit  434 . For such class(es), the tap coefficient decision circuit  435  outputs e.g., default tap coefficients. 
     The coefficient memory  436  memorizes the class-based tap coefficients, pertinent to linear prediction coefficients and residual signals, supplied from the tap coefficient decision circuit  435 . 
     In the above-described learning device, the processing similar to the processing conforming to the flowchart shown in  FIG. 29  is performed to find tap coefficients for obtaining the synthesized sound of high sound quality. 
     That is, the learning device is fed with speech signals for learning and, at step S 211 , teacher data and pupil data are generated from these speech signals for learning. 
     That is, the speech signals for learning are input to the microphone  501 . The components from the microphone  501  to the code decision unit  515  perform the processing similar to that performed by the microphone  1  to the code decision unit  15  of  FIG. 1 . 
     The result is that the speech of digital signals, obtained in the A/D converter  502 , is sent as teacher data to the normal equation addition circuit  434 . The synthesized sound, output by the speech synthesis filter  506  when the minimum square error decision unit  508  has verified that the square error has become smallest, is sent as pupil data to the tap generators  431 ,  432 . The L, G, I and A codes, output by the code decision unit  515  when the minimum square error decision unit  508  has verified that the square error has become smallest, are also sent as pupil data to the tap generators  431 ,  432 . 
     The program then moves to step S 212  where the tap generator  431  generates prediction taps, with the frame of the synthesized sound sent as pupil data from the speech synthesis filter  506  as the frame of interest, from the L, G, I and A codes and the synthesized sound of the frame of interest, to route the so produced prediction taps to the normal equation addition circuit  434 . At step S 212 , the tap generator  432  also generates class taps from the L, G, I and A codes and the synthesized sound of the frame of interest, to send the so generated class taps to the classification unit  433 . 
     After processing at step S 212 , the program moves to step S 213 , where the classification unit  433  performs classification based on the class taps from the tap generator  432  to send the resulting class codes to the normal equation addition circuit  434 . 
     The program then moves to step S 214 , where the normal equation addition circuit  434  performs the aforementioned summation of the matrix A and the vector v of the equation (13), for the speech of high sound quality of the frame of interest from the A/D converter  502 , as teacher data, and for the prediction taps from the tap generator  432 , as pupil data, for each class code from the classification unit  433 . The program then moves to step S 215 . 
     At step S 215 , it is verified whether or not there is any speech signal for learning for the frame to be processed as the frame of interest. If it is verified at step S 215  that there is any speech signal for learning of the frame to be processed as the frame of interest, the program reverts to step S 211  where the next frame is set as a new frame of interest. The processing similar to that described above then is repeated. 
     If it is verified at step S 215  that there is no speech signal for learning of the frame to be processed as the frame of interest, that is if the normal equation is obtained in each class in the normal equation addition circuit  434 , the program moves to step S 216  where the tap coefficient decision circuit  435  solves the normal equation generated for each class to find the tap coefficients for each class. These tap coefficients are sent to and stored in the address in the coefficient memory  436  associated with each class to terminate the processing. 
     The class-based tap coefficients, are stored in the coefficient memory  436 , are stored in the coefficient memory  224  of  FIG. 32 . 
     Consequently, the tap coefficients stored in the coefficient memory  224  of  FIG. 32  have been found on learning so that the prediction errors of the prediction values of the true speech of high sound quality, obtained on carrying out linear predictive calculations, herein square errors, will be statistically minimum, so that the speech output by the prediction unit  225  of  FIG. 32  is of high sound quality. 
     In the instances shown in  FIGS. 32 and 33 , the class taps are generated from the synthesized sound output by the speech synthesis filter  506  and the L, G, I and A codes. Alternatively, the class taps may also be generated from one or more of and the L, G, I and A codes and from the synthesized sound output by the speech synthesis filter  506 . The class taps may also be formed from linear prediction coefficients α p  obtained from the A code, the information obtained from the L, G, I or A code, inclusive of the gain values β, γ obtained from the G code, such as residual signals e, or 1, n for producing the residual signals e or with 1/β or n/γ, as shown with dotted lines in  FIG. 32 . The class taps may also be produced from the synthesized sound output by the speech synthesis filter  506  or the above-mentioned information derive from the L, G, I or A code. In cases where software interpolation bits or the frame energy are contained in the code data in the CELP system, the class taps may be formed using the soft interpolation bits or the frame energy. The same may be said of the prediction taps. 
       FIG. 34  shows speech signals s, used as teacher data, data ss of the synthesized sound used as pupil data, residual signals e and n, 1 used for finding the residual signals e in the learning device of  FIG. 33 . 
     The above-described sequence of operations may be carried out by software or by hardware. If the sequence of operations is carried out by software, the program forming the software is installed on e.g., a general-purpose computer. 
     The above-described sequence of operations may be carried out by software or by hardware. If the sequence of operations is carried out by software, the program forming the software is installed on e.g., a general-purpose computer. 
     The computer on which is installed the program for executing the above-described sequence of operations is configured as shown in  FIG. 13 , as described above, and the operation similar to that performed by the computer shown in  FIG. 13  is executed, and hence is not explained specifically for simplicity. 
     In the present invention, the processing step for stating the program for executing the various processing operations by a computer need not be carried out chronologically in the order stated in the flowchart, but may be processed in parallel or batch-wise, such as parallel processing or object-based processing. 
     The program may be processed by a sole computer or by plural computers in a distributed fashion. Moreover, the program may be transmitted to a remotely located computer for execution. 
     Although no particular reference has been made in the present invention as to which sort of the speech signals for learning is to be used, the speech signals for learning may not only be the speech uttered by a speaker but may also be a musical number (music). If, in the above-described learning, the speech uttered by a speaker is used as the speech signals for learning, such tap coefficients which will improve the sound quality of the speech may be obtained, whereas, if the speech signals for learning are music numbers are used, such tap coefficients may be obtained which will improve the sound quality of the musical number. 
     The present invention may be broadly applied in generating the synthesized sound from the code obtained on encoding by the CELP system, such as VSELP (Vector Sum Excited Linear Prediction), PSI-CELP (Pitch Synchronous Innovation CELP), CS-ACELP (Conjugate Structure Algebraic CELP). 
     The present invention also is broadly applicable not only to such a case where the synthesized sound is generated from the code obtained on encoding by CELP system but also to such a case where residual signals and linear prediction coefficients are obtained from a given code to generate the synthesized sound. 
     In the above-described embodiment, the prediction values of residual signals and linear prediction coefficients are found by one-dimensional linear predictive calculations. Alternatively, these prediction values may be found by two-or higher dimensional predictive calculations. 
     In the above explanation, the classification is carried out by vector quantizing the class taps. Alternatively, the classification may also be carried out by exploiting e.g., the ADRC processing. 
     In the classification employing the ADRC, the elements making up the class tap, that is sampled values of the synthesized sound, or L, G, I and A codes, are processed with ADRC, and the class is determined in accordance with the resulting ADRC code. 
     In the K-bit ADRC, the maximum value MAX and the minimum value MIN of the elements, forming the class tap, are detected, DR=MAX−MIN is set as the local dynamic range of the set, and the elements forming the class taps are re-quantized into K bits. That is, the minimum value MIN is subtracted from the respective elements forming the class tap, and the resulting difference value is divided by DR/2K. The values of the K bits of the respective elements, forming the class tap, obtained as described above, are arrayed in a preset sequence into a bit string, which is output as an ADRC code. 
     INDUSTRIAL APPLICABILITY 
     According to the present invention, described above, the prediction taps used for predicting the speech of high sound quality, as target speech, the prediction values of which are to be found, are extracted from the synthesized sound or from the code or the information derived from the code, whilst the class taps used for sorting the target speech to one of plural classes are extracted from the synthesized sound, code or the information derived from the code. The class of the target speech is found based on the class taps. Using the prediction taps and the tap coefficients corresponding to the class of the target speech, the prediction values of the target speech are found to generate the synthesized sound of high sound quality.