Abstract:
In a speech recognition apparatus, a feature extracting portion extracts feature parameters by sliding a plurality of frames corresponding to time windows each having a prescribed length of time with a successively increasing time width, over an input speech signal. A word lexicon database stores standard pattern data in correspondence with phoneme patterns of the input speech. A recognition processing portion collates the feature parameter extracted by the feature extracting portion with the standard pattern data to recognize a corresponding phoneme, and outputs a recognition result.

Description:
BACKGROUND OF THE INVENTION  
       [0001]     1. Field of the Invention  
         [0002]     The present invention relates to a configuration of a speech recognition apparatus based on phoneme-by-phoneme recognition.  
         [0003]     2. Description of the Background Art  
         [0004]     Conventionally, speech recognition in speech recognition apparatuses is in most cases realized by transforming speech to a time sequence of features and by comparing the time sequence with a time sequence of a standard pattern prepared in advance.  
         [0005]     By way of example, Japanese Patent Laying-Open No. 2001-356790 discloses a technique in which a feature values extracting part extracts voice feature values from a plurality of time windows of a constant length set at every prescribed period, from the voice as an object of analysis, in a voice recognition device that enables machine-recognition of human speech. According to this technique, a frequency axial series feature parameter concerning the frequency of the voice and a power series feature parameter concerning the amplitude of the voice are extracted in different cycles, respectively.  
         [0006]     Japanese Patent Laying-Open No. 5-303391 discloses a technique in which a plurality of units of time (frames) for computing feature parameters are prepared, or prepared phoneme by phoneme, feature parameter time sequences are computed for respective frame lengths and phoneme collating is performed on each of the time sequences, and the optimal one is selected.  
         [0007]     In the above described methods in which a plurality of time windows of a constant length are shifted at every prescribed time period while the voice is transformed to time sequences of features, the number of extracted feature parameters may differ dependent on the length of phonemes. As a result, the number of parameters affects the recognition rate.  
       SUMMARY OF THE INVENTION  
       [0008]     An object of the present invention is to provide a speech recognition apparatus employing a method of computing feature parameters that can improve recognition rate of each phoneme.  
         [0009]     The speech recognition apparatus of the present invention includes a feature extracting portion, a storage portion and a recognizing portion. The feature extracting portion extracts a feature parameter by sliding, at least with different time width, a plurality of frames corresponding to time windows each having a prescribed time length, over an input speech signal. The storage portion stores standard pattern data in correspondence with phonetic patterns of the input speech. The recognizing portion collates the feature parameter extracted by the feature extracting portion with the standard pattern data to recognize the corresponding phoneme, and outputs the result of recognition.  
         [0010]     According to the speech recognition apparatus of the present invention, it is possible to improve recognition rate of each phoneme, no matter whether average duration of phonemes is long or short, with reduced burden on processing.  
         [0011]     The foregoing and other objects, features, aspects and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0012]      FIG. 1  is a functional block diagram representing the configuration of a speech recognition apparatus  10 .  
         [0013]      FIG. 2  is a schematic illustration representing frame shift by a feature detecting portion  102  shown in  FIG. 1 .  
         [0014]      FIG. 3  is a functional block diagram representing the configuration of a speech recognition apparatus  100 .  
         [0015]      FIG. 4  is a schematic illustration representing a frame shift operation by a feature parameter computing portion  3021  of speech recognition apparatus  100 .  
         [0016]      FIG. 5 . is a functional block diagram representing a configuration of a speech recognition apparatus  200  in accordance with a second embodiment.  
         [0017]      FIG. 6  is a functional block diagram representing a configuration of a speech recognition apparatus  300  in accordance with a fourth embodiment.  
         [0018]      FIG. 7  is a functional block diagram representing a configuration of a speech recognition apparatus  400  in accordance with a sixth embodiment.  
         [0019]      FIG. 8  is a schematic illustration representing how the standard pattern is stored in a first word lexicon database  6022 .  
         [0020]      FIG. 9  is a schematic illustration representing a process performed by a data interpolating portion  6032 ;  
         [0021]      FIG. 10  is a functional block diagram representing a configuration of a speech recognition apparatus  500  in accordance with an eighth embodiment. 
     
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0022]     Embodiments of the present invention will be described with reference to the figures.  
         [0023]     (Basic Background for Better Understanding of the Present Invention)  
         [0024]     As a basic background for help better understanding the configuration of the speech recognition apparatus in accordance with the present invention, configuration and operation of a common speech recognition apparatus  10  will be described.  
         [0025]      FIG. 1  is a functional block diagram representing the configuration of such a speech recognition apparatus  10 .  
         [0026]     Referring to  FIG. 1 , a feature detecting portion  102  computes feature parameters such as an LPC cepstrum coefficient (Fourier transform of logarithmic power spectrum envelope for each frame as a unit of speech segmentation of several tens of milliseconds) for input speech applied as an input. Specifically, when feature detecting portion computes a feature, it typically uses several milliseconds or several tens of milliseconds as a unit time (frame), and computes the feature approximating that the feature, that is the structure of acoustic wave, is in a steady state within the time period of one frame. Thereafter, the feature parameter is again computed with the frame shifted by a certain time period (which operation is referred to as a “frame shift”). By repeating these operations, a time sequence of feature parameter is obtained.  
         [0027]     A recognizing portion  103  compares the time sequence of feature parameter obtained in this manner with a standard pattern in a word lexicon database (word lexicon DB)  104  stored in a storage apparatus, computes similarity, and outputs a recognition result  105 .  
         [0028]      FIG. 2  is a schematic illustration representing the frame shift by a feature detecting portion  102  shown in  FIG. 1 .  
         [0029]     As can be seen from  FIG. 2 , in feature detecting portion  102  of speech recognition apparatus  10 , time width D 201  of frame shift is constant. Therefore, words having long phonetic duration and words having short phonetic duration come to have different number of feature parameters. Accordingly, there arises a tendency that a word with a long phoneme has higher recognition rate while a word with a short phoneme has recognition rate lower than that of the word with a long phoneme.  
         [0030]     In the present invention, the feature parameters are computed while the time width of frame shift is made variable, so that the same number of feature parameters are generated both for words with long phoneme and words with short phoneme, focusing on portions that are considered critically important in phoneme analysis, as will be described in the following.  
         [heading-0031]     [First Embodiment] 
         [0032]     The configuration and operation of a speech recognition apparatus  100  in accordance with the first embodiment of the present invention will be described in the following.  
         [0033]      FIG. 3  is a functional block diagram representing the configuration of a speech recognition apparatus  100 .  
         [0034]     The configuration of speech recognition apparatus  100  is basically the same as speech recognition apparatus  10  shown in  FIG. 1 .  
         [0035]     It is noted, however, that at a feature extracting portion  302  receiving an input speech  301  that is a digitized speech of a speaker, a feature parameter computing portion  3021  makes frame shift interval denser for frame intervals at beginning portions of phonemes and makes the frame intervals gradually coarser toward terminating portions of words while computing the feature parameters. Further, a word lexicon database  304 , which is referred to by recognition processing portion  303  for performing the recognizing process after receiving a time sequence of feature parameters computed in this manner, is adapted to store in advance standard patterns corresponding to the frame intervals varying in accordance with a prescribed rule to meet the variable frame intervals, as will be described later. Recognition processing portion  303  refers to word lexicon database as such, performs recognition by collating with the time sequence of feature parameters, and outputs the recognition result.  
         [0036]     The operation of speech recognition apparatus  100  will be described in detail in the following.  
         [0037]     For phoneme recognition, average duration of each phoneme is important. Features of the phoneme can roughly be classified into beginning, middle and ending portions of words. Consonants represented by pronunciation symbols such as /t/ and /r/ have as short an average duration as about 15 milliseconds at the beginning, middle and ending portions of a word, while vowels have an average duration as long as 100 milliseconds or longer. When various phonemes with such significantly different durations are to be recognized, the data at the initial part of a word is of critical importance. Therefore, in the present invention, time width of the frame shift is changed in accordance with a prescribed rule that will be described below.  
         [0038]      FIG. 4  is a schematic illustration representing a frame shift operation by a feature parameter computing portion  3021  of speech recognition apparatus  100 .  
         [0039]     By way of example, in  FIG. 4 , it is assumed that from an input speech  301  that has been quantized with 16 bits at a sampling frequency of 20 KHz, feature parameters are computed at a feature parameter computing portion  3021 .  
         [0040]     Feature parameter computing portion  3021  shifts a fixed frame length L that is a time window, with time widths D 301  to D 30   n  (e.g.: D 301 &lt;D 302 &lt;D 303 &lt; . . . &lt;D 30   n, n : natural number) that become gradually longer from the starting portion to the end of the input speech, and generates feature parameter time sequences S 1  to Sn.  
         [0041]     When time widths D 301  to D 30   n  are made gradually longer, the time interval D 301  from the head frame to the next frame may be used as a reference, and the following time intervals D 302  to D 30   n  may be gradually made longer in geometric series at a prescribed rate, or the following time intervals D 302  to D 30   n  may be gradually made longer in arithmetic series with a prescribed interval, though not limiting. Alternatively, the following time intervals D 302  to D 30   n  may be gradually made longer in more general manner, in accordance with a function that monotonously increases with time.  
         [0042]     First, data of the frame length L from the beginning of the input speech  301  is considered, and a feature parameter is computed assuming that the data in this range are in a steady state. For instance, from a 12th order linear predictive coding (LPC), a 16th order LPC cepstrum coefficient is computed and made to a 16-dimensional feature vector. Thereafter, the frame is shifted with the time width of D 30   i  (i=1 to n), and the feature vector is computed in the similar manner. This operation is repeated to the end of input speech  301 , and a time sequence Sn of feature parameters computed with the fixed frame length L is obtained.  
         [0043]     When the feature parameters are output from feature parameter computing portion  3021 , parameters are compared with word lexicon database  304  frame by frame. After all the frames are compared, a most suitable model that satisfies a threshold value among the models registered in word lexicon database  304  is output as the recognition result  305 .  
         [0044]     Here, as the data to be stored in word lexicon database  304 , standard patterns are prepared beforehand, using feature parameters computed with frame shift of time widths D 301  to D 30   n  with the frame length of L, for individual phoneme models. Such standard patterns are formed by preparing and training individual Hidden Markov Model (HMM) P 01 , with the feature parameter time sequences computed using a speech database of which contents of speech and phonetic periods are known in advance. Word lexicon database  304  is configured by the HMM model of the phoneme number M (M: prescribed natural number) obtained in this manner.  
         [0045]     Recognizing processing portion  303  checks position of presence and probability of presence of all the phonemes, and of those overlapping in position of presence, one having higher probability of presence is left. A sequence of phonemes obtained in this manner is output as recognition result  305 .  
         [0046]     By speech recognition apparatus having the above-described configuration, it becomes possible to improve recognition rate by increasing weight of a feature parameter corresponding to the beginning portion of a phoneme, as compared with the phoneme recognition rate with the time width of frame shift being fixed.  
         [0047]     [Second Embodiment] 
         [0048]      FIG. 5  is a functional block diagram representing a configuration of a speech recognition apparatus  200  in accordance with a second embodiment.  
         [0049]     In the following, the process procedure of extracting feature parameters while the interval between frames as the time window is fixed will be referred to as “fixed-frame-interval extraction process.” 
         [0050]     Speech recognition apparatus  200  shown in  FIG. 5  includes a first feature extracting portion  402  having a first feature parameter computing portion performing the fixed-frame-interval extraction process at a first time interval on a digitized input speech  401 , and a second feature extracting portion  403  having a second feature parameter computing portion performing the fixed-frame-interval extraction process at a second time interval.  
         [0051]     By the first and second feature extracting portions  402  and  403 , first feature parameter time sequences S 01  to S 0   n  and second feature parameter time sequences S 11  to S 1   n  are computed.  
         [0052]     Speech recognition apparatus  200  further includes a first word lexicon database  4022  having phoneme models corresponding to the fixed-frame-interval extraction process with the first time interval registered in advance, a second word lexicon database  4032  having phoneme models corresponding to the fixed-frame-interval extraction process with the second time interval registered in advance, a first recognition processing portion  4021  comparing each of the feature parameters computed by the first feature extracting portion  402  with data in the first word lexicon database  4022 , a second recognition processing portion  4031  comparing each of the feature parameters computed by the second feature extracting portion  403  with data in the second word lexicon database  4032 , and a result selecting portion  404  selecting recognition result of first or second recognition processing portion  4021  or  4031  in accordance with relevance thereof and obtaining a recognition result  405 .  
         [0053]     The operation of speech recognition apparatus  200  will be described in grater detail in the following.  
         [0054]     First, data of the frame length L from the beginning of the input speech  401  is considered, and a feature parameter is computed by first and second feature extracting portions  402  and  403 , assuming that the data in this range are in a steady state.  
         [0055]     In speech recognition apparatus  200 , from a 12th order linear predictive coding LPC, a 16th order LPC cepstrum coefficient is computed and made to a 16-dimensional feature vector by the first feature extracting portion  402 . Similarly, from a 12th order linear predictive coding LPC, a 16th order LPC cepstrum coefficient is computed and made to a 16-dimensional feature vector by the second feature extracting portion  403 .  
         [0056]     As a result, first and second feature parameters S 01  and S 11  are obtained at the first and second feature parameter extracting portions  402  and  403 , respectively. After this operation, until the end of input speech  401 , the first feature extracting portion  402  outputs first feature parameters SOn computed with frame shift repeated at a fixed time width D 201 , and the second feature extracting portion  403  outputs second feature parameters Sln computed with fame shift repeated at a fixed time width D 2011  (&lt;D 201 ).  
         [0057]     On the other hand, first standard patterns are formed beforehand for each phoneme model, using feature parameters computed from the frame length L. The first standard patterns are formed by preparing and training individual Hidden Markov Model (HMM) P 01 , with the feature parameter time sequences computed using a speech database of which contents of speech and phonetic periods are known in advance (here, the feature parameter time sequences are formed with the time width of frame shift set to D 201 ). First word lexicon database  4022  is configured by the HMM model of the phoneme number M obtained in this manner.  
         [0058]     Further, second standard patterns are similarly formed beforehand, using feature parameters computed from the frame length L. The second standard patterns are formed by preparing and training individual Hidden Markov Model (HMM) P 11 , with the feature parameter time sequences computed using a speech database of which contents of speech and phonetic periods are known in advance (here, the feature parameter time sequences are formed with the time width of frame shift set to D 2011 ). Second word lexicon database  4032  is configured by the HMM model of the phoneme number M obtained in this manner.  
         [0059]     At the first recognition processing portion  4021 , collation is performed for feature parameter time sequence S 01  using standard patterns P 01  and for feature parameter time sequence S 02  using standard patterns P 02 , starting from the initial frame of the input speech, phoneme by phoneme. In the similar manner, phoneme collation is performed for the feature parameter time sequence S 0   n  using standard patterns P 0   n , and those of which position of presence and probability of presence overlap are output.  
         [0060]     Similarly, at the second recognition processing portion  4031 , collation is performed for feature parameter time sequence S 11  using standard patterns P 11  and for feature parameter time sequence S 12  using standard patterns P 12 , starting from the initial frame of the input speech, phoneme by phoneme. In the similar manner, phoneme collation is performed for the feature parameter time sequence S 1   n  using standard patterns P 1   n , and those of which position of presence and probability of presence overlap are output.  
         [0061]     Result selecting portion  404  checks position of presence and probability of presence for all the phonemes output from the first and second recognition processing portions  4021  and  4031 , and of those overlapping in position of presence, one having higher probability of presence is left. Result selecting portion  404  outputs a sequence of phonemes obtained in this manner as recognition result  405 .  
         [0062]     By speech recognition apparatus  200  having the above-described configuration, it becomes possible to improve recognition rate by using feature parameters extracted with different time intervals between frames and selecting results with higher probability of presence, as compared with the phoneme recognition rate with the time width of frame shift being fixed.  
         [0063]     [Third Embodiment] 
         [0064]     In the following, a process procedure of extracting feature parameters while the interval between frames as the time window is made successively longer will be referred to as “variable-frame-interval extraction process.” 
         [0065]     In the second embodiment, it has been assumed that both the first and second feature extracting portions  402  and  403  perform the fixed-frame-interval extraction process.  
         [0066]     Basic configuration of the speech recognition apparatus in accordance with the third embodiment of the present invention is the same as that of speech recognition apparatus  200  in accordance with the second embodiment.  
         [0067]     It is noted, however, that the second feature extracting portion  403  performs the variable-frame-interval extraction process.  
         [0068]     Specifically, the second feature extracting portion  403  varies the time interval of frame shift D 30   i  (i: natural number, D 301 &lt;D 302 &lt;D 303 &lt; . . . ) to be gradually longer, while computing respective feature parameters.  
         [0069]     For the second word lexicon database  4032 , standard patterns are prepared beforehand, using feature parameters computed with the time width of frame shift set at D 30   i  (i: natural number, D 301 &lt;D 302 &lt;D 303 &lt; . . . ).  
         [0070]     Other configurations of the speech recognition apparatus in accordance with the third embodiment are the same as those of speech recognition apparatus  200  in accordance with the second embodiment, and therefore, description thereof will not be repeated.  
         [0071]     By the speech recognition apparatus in accordance with the third embodiment having such a configuration, it becomes possible to effectively handle phonemes having long average duration by the fixed-frame-interval extracting process and to effectively handle phonemes having short average duration by the variable-frame-interval extracting process, and therefore, in addition to the effects attained by speech recognition apparatus  200 , a further effect of alleviating burden of processing can be attained.  
         [0072]     [Fourth Embodiment] 
         [0073]      FIG. 6  is a functional block diagram representing a configuration of a speech recognition apparatus  300  in accordance with a fourth embodiment.  
         [0074]     Speech recognition apparatus  300  shown in  FIG. 6  includes a first feature extracting portion  502  having a first feature parameter computing portion performing the fixed-frame-interval extraction process at a first time interval, and a second feature extracting portion  503  having a second feature parameter computing portion performing the fixed-frame-interval extraction process at a second time interval, on a digitized input speech  501 .  
         [0075]     Speech recognition apparatus  300  further includes an inverter  511  receiving as an input a control signal  51  that will be described later, and an input selecting portion  510  responsive to control signal  51  and an output signal  50  of inverter  511  to selectively apply the input speech  501  either to the first feature extracting portion  502  or the second feature extracting portion  503 .  
         [0076]     Input selecting portion  510  includes an AND circuit  512  receiving input speech  501  and control signal  51  at inputs and providing an output to the first feature extracting portion  502 , and an AND circuit  513  receiving input speech  501  and output  50  of inverter  511  and providing an output to the second feature extracting portion  503 .  
         [0077]     The first and second feature extracting portions  502  and  503  compute the first and second feature parameter time sequences S 01  to S 0   n  and S 11  to S 1   n , respectively.  
         [0078]     Speech recognition apparatus  300  further includes a first word lexicon database  5022  having phoneme models corresponding to the fixed-frame-interval extracting process at the first time interval registered in advance, a second word lexicon database  5032  having phoneme models corresponding to the fixed-frame-interval extracting process at the second time interval registered in advance, a first recognition processing portion  5021  comparing each of the feature parameters computed by the first feature extracting portion  502  with the data in the first word lexicon database  5022  for phoneme recognition, a second recognition processing portion  5031  comparing each of the feature parameters computed by the second feature extracting portion  503  with the data in the second word lexicon database  5032  for phoneme recognition, and a result selecting portion  504  selecting recognition results of the first and second recognition processing portions  5021  and  5031  in accordance with the procedure described in the following to obtain a recognition result  505 .  
         [0079]     Result selecting portion  504  includes an AND circuit  514  receiving an output of the first recognition processing portion  5021  and control signal  51  at inputs and outputting recognition result  505 , and an AND circuit  515  receiving an output of the second recognition processing portion  5031  and output signal  50  and outputting recognition result  505 .  
         [0080]     The operation of speech recognition apparatus  300  will be described in the following.  
         [0081]     First, data of the frame length L from the beginning of the input speech  501  is considered, and a feature parameter is computed by first or second feature extracting portion  502  or  503  in response to control signal  51 , assuming that the data in this range are in a steady state.  
         [0082]     Here, it is assumed that control signal  51  changes such that in the recognition process at the first recognition processing portion  5021 , when a threshold value set for obtaining a recognition result is satisfied, the speech is input to the first feature extracting portion  502 , and when the threshold value is not satisfied by the first recognition processing portion  5021 , the speech is input to the second feature extracting portion  503 .  
         [0083]     By way of example, consider an input speech  501  of which beginning part of the word is the same as some of the registered words, while ending part is different. In such a case, in the first processing system consisting of the first feature extracting portion  502  and the first recognition processing portion  5021 , it becomes less and less likely that the threshold value is satisfied, when the recognition process is performed frame by frame from the beginning part to the ending part of the word.  
         [0084]     At this time, the first recognition processing portion  5021  returns a control flag as control signal  51 , and by that flag, the recognition process is switched to the second processing system consisting of the second feature extracting portion  503  and the second recognition processing portion  5031 , whereby the recognition process is performed with the shift time width varied.  
         [0085]     In the following, description will be given assuming that in the fourth embodiment, the time width of frame shift in the second processing system mentioned above is shorter than the time width of frame shift in the first processing system.  
         [0086]     In the fourth embodiment, from a 12th order linear predictive coding LPC, a 16th order LPC cepstrum coefficient is computed and made to a 16-dimensional feature vector, by the first and second feature extracting portions  502  and  503 .  
         [0087]     As a result, the first feature parameter S 01  and the second feature parameter S 11  are obtained at the first and second feature extracting portions  502  and  503 , respectively. After this operation, until the end of the input signal, the first feature extracting portion  502  outputs first feature parameters S 0   n  computed with frame shift repeated at a fixed time width D 201 , and the second feature extracting portion  503  outputs second feature parameters S in computed with fame shift repeated at a fixed time width D 2011  (&lt;D 201 ).  
         [0088]     As in the second embodiment, it is assumed that the first and second word lexicon databases  5022  and  5033  store the first and second standard patterns consisting of HMM models for respective phoneme models, which correspond to the feature parameter time sequences formed with the time width of frame shift set to D 201  and the feature parameter time sequences formed with the time width of frame shift set to D 2011 , respectively.  
         [0089]     The first recognition processing portion  5021  uses standard pattern P 01  for the feature parameter time sequence S 01  and standard pattern P 02  for the second feature parameter time sequence S 02 , frame by frame, starting from the initial frame of the input speech. Similarly, the first recognition processing portion  5021  uses standard pattern POx (x: natural number) for the feature parameter time sequence S 0   x , and outputs those satisfying the overlapping of position of presence and probability of presence, and the set threshold value. When the set threshold value is not satisfied while this process is repeated, the first recognition processing portion  5021  generates a switching signal to invert control signal  51 , whereby the process is switched such that phoneme collation is performed by the second recognition processing portion  5031  using outputs of the second feature extracting portion  503 . Specifically, after switching, the second recognition processing portion  5031  uses standard pattern P 1 ( x+ 1) for the feature parameter time sequence S 1 ( x+ 1) and standard pattern P 1 ( x+ 2) for the second feature parameter time sequence S 1 ( x+ 2), frame by frame, thereafter, uses standard pattern P 1   n  for the feature parameter time sequence S 1   n  in the similar manner, to perform phoneme collation, and outputs those that overlap in the position of presence and probability of presence.  
         [0090]     Then, result selecting portion  504  outputs a phoneme sequence resulting from the processing by the first or second processing systems as the final recognition result  505 .  
         [0091]     By speech recognition apparatus  300  having the above-described configuration in accordance with the fourth embodiment, it becomes possible to improve recognition rate, as compared with the phoneme recognition rate with the time width of frame shift being fixed.  
         [0092]     As another effect, it is possible that another processing system, not shown, is provided, which system is not specifically limited, a signal may be generated indicating that said another processing system is in operation, and the signal may be used as the control signal  51 . By such an approach, it becomes possible in the system including the speech signal processing apparatus  300  to alleviate the processing burden of a CPU (Central Processing Unit).  
         [0093]     [Fifth Embodiment] 
         [0094]     In the fourth embodiment, it is assumed that both the first and second feature extracting portions  502  and  503  perform the fixed-frame-interval feature extracting process.  
         [0095]     Basic configuration of the speech recognition apparatus in accordance with the fifth embodiment of the present invention is the same as that of speech recognition apparatus  300  in accordance with the fourth embodiment.  
         [0096]     It is noted, however, that the second feature extracting portion  503  performs the variable-frame-interval extraction process, in the speech recognition apparatus in accordance with the fifth embodiment.  
         [0097]     Specifically, the second feature extracting portion  503  varies the time width of frame shift D 30   i  (i: natural number, D 301 &lt;D 302 &lt;D 303 &lt; . . . ) to be gradually longer, while computing respective feature parameters, as described with reference to  FIG. 4 .  
         [0098]     For the second word lexicon database  5032 , standard patterns are prepared beforehand, using feature parameters computed with the time width of frame shift set at D 30   i  (i: natural number, D 301 &lt;D 302 &lt;D 303 &lt; . . . ).  
         [0099]     Other configurations of the speech recognition apparatus in accordance with the fifth embodiment are the same as those of speech recognition apparatus  300  in accordance with the fourth embodiment, and therefore, description thereof will not be repeated.  
         [0100]     By the speech recognition apparatus in accordance with the fifth embodiment having such a configuration, it becomes possible to effectively handle phonemes having long average duration by the fixed-frame-interval extracting process and to effectively handle phonemes having short average duration by the variable-frame-interval extracting process, and therefore, in addition to the effects attained by speech recognition apparatus  300 , a further effect of alleviating burden of processing can be attained.  
         [0101]     [Sixth Embodiment] 
         [0102]      FIG. 7  is a functional block diagram representing a configuration of a speech recognition apparatus  400  in accordance with a sixth embodiment.  
         [0103]     In speech recognition apparatus  400  shown in  FIG. 7 , an input speech  601 , an input selecting portion  610 , a control signal  61 , an inverter  611 , a first feature extracting portion  602 , a second feature extracting portion  603 , a first recognition processing portion  6021 , a second recognition processing portion  603 . 1 , a result selecting portion  604 , a first word lexicon database  6022  and recognition result  605  respectively have functions corresponding to input speech  501 , input selecting portion  510 , control signal  51 , inverter  511 , first feature extracting portion  502 , second feature extracting portion  503 , first recognition processing portion  5021 , second recognition processing portion  5031 , result selecting portion  504 , first word lexicon database  5022  and recognition result  505 , of speech recognition apparatus  300  in accordance with the fourth embodiment.  
         [0104]     In speech recognition apparatus  400  shown in  FIG. 7 , different from the configuration of speech recognition apparatus  300  in accordance with the fourth embodiment, a data interpolating portion  6032  is provided in place of the second word lexicon database  5032 .  
         [0105]     It is also assumed in speech recognition apparatus  400  shown in  FIG. 7 , that the time width D 2011  of frame shift in the second processing system consisting of the second feature extracting portion  503  and the second recognition processing portion  5031  is shorter than the time width D 201  of frame shift in the first processing system consisting of the first feature extracting portion  502  and the first recognition processing portion  5021 .  
         [0106]     Here, also in speech recognition apparatus  400 , first standard patterns are formed beforehand for each phoneme model, using feature parameters computed from the frame length L. The first standard patterns are formed by preparing and training individual Hidden Markov Model (HMM) P 01 , with the feature parameter time sequences computed using a speech database of which contents of speech and phonetic periods are known in advance (here, the feature parameter time sequences are formed with the time width of frame shift set to D 201 ). Thus, first word lexicon database  6022  is configured by the HMM model of the phoneme number M obtained in this manner.  
         [0107]      FIG. 8  is a schematic illustration representing how the standard pattern is stored in a first word lexicon database  6022 .  
         [0108]     As shown in  FIG. 8 , for the HMM model corresponding to phonemes, the first standard patterns  801  to  80   n  in a prescribed time period are provided as parameters m1 to mn at time points t1 to tn, respectively.  
         [0109]     In speech recognition apparatus  400 , time width D 2011  of frame shift in the second processing system is shorter that time width D 201  of frame shift D 201  for the first processing system, and therefore, even when the first standard patterns are to be used as the second standard patterns for the second recognition processing portion  5031 , some portions are missing for the second standard patterns, in the first word lexicon database  6022 .  
         [0110]     Therefore, in speech recognition apparatus  400 , the second standard patterns are generated by interpolating portion  6032 , based on the first standard patterns.  
         [0111]      FIG. 9  is a schematic illustration representing a process performed by a data interpolating portion  6032 .  
         [0112]     As shown in  FIG. 9 , the second standard pattern at every time point can be formed by computing the intermediate data by linear interpolation (or by any function of high order), using the first standard patterns and time data.  
         [0113]     Other configurations of the speech recognition apparatus  400  are the same as those of the fourth embodiment, and therefore, description thereof will not be repeated.  
         [0114]     By the configuration of speech recognition apparatus  400  as described above, it becomes possible to reduce storage capacity of a storage apparatus such as a memory used as the word lexicon database.  
         [0115]     [Seventh Embodiment] 
         [0116]     In the sixth embodiment, it is assumed that both the first and second feature extracting portions  602  and  603  perform the fixed-frame-interval feature extracting process.  
         [0117]     Basic configuration of the speech recognition apparatus in accordance with the seventh embodiment of the present invention is the same as that of speech recognition apparatus  400  in accordance with the sixth embodiment.  
         [0118]     It is noted, however, that the second feature extracting portion  603  performs the variable-frame-interval extraction process, in the speech recognition apparatus in accordance with the seventh embodiment.  
         [0119]     Specifically, the second feature extracting portion  603  varies the time width of frame shift D 30   i  (i: natural number, D 301 &lt;D 302 &lt;D 303 &lt; . . . ) to be gradually longer, while computing respective feature parameters, as described with reference to  FIG. 4 .  
         [0120]     In generating the second standard patterns, all the standard patterns are formed by data interpolating portion  6032  using the first word lexicon database  6022 , as in the sixth embodiment.  
         [0121]     Other configurations of the speech recognition apparatus in accordance with the seventh embodiment are the same as those of speech recognition apparatus  400  in accordance with the sixth embodiment, and therefore, description thereof will not be repeated.  
         [0122]     By the speech recognition apparatus in accordance with the seventh embodiment having such a configuration, it becomes possible to effectively handle phonemes having long average duration by the fixed-frame-interval extracting process and to effectively handle phonemes having short average duration by the variable-frame-interval extracting process, and therefore, in addition to the effects attained by speech recognition apparatus  300 , a further effect of alleviating burden of processing can be attained.  
         [0123]     [Eighth Embodiment] 
         [0124]      FIG. 10  is a functional block diagram representing a configuration of a speech recognition apparatus  500  in accordance with an eighth embodiment.  
         [0125]     In the configuration of speech recognition apparatus  500  shown in  FIG. 10 , an input speech  701 , an input selecting portion  710 , a control signal  71 , an inverter  711 , a first feature extracting portion  702 , a second feature extracting portion  703 , a first recognition processing portion  7021 , a second recognition processing portion  7031 , a result selecting portion  704 , a first word lexicon database  7022  and recognition result  705  respectively have functions corresponding to input speech  601 , input selecting portion  610 , control signal  61 , inverter  611 , first feature extracting portion  602 , second feature extracting portion  603 , first recognition processing portion  6021 , second recognition processing portion  6031 , result selecting portion  604 , first word lexicon database  6022  and recognition result  605  of speech recognition apparatus  400  in accordance with the sixth embodiment.  
         [0126]     Here, also in speech recognition apparatus  500 , first standard patterns are formed beforehand for each phoneme model, using feature parameters computed from the frame length L. The first standard patterns are formed by preparing and training individual Hidden Markov Model (HMM) P 01 , with the feature parameter time sequences computed using a speech database of which-contents of speech and phonetic periods are known in advance (here, the feature parameter time sequences are formed with the time width of frame shift set to D 201 ). Thus, first word lexicon database  7022  is configured by the HMM model of the phoneme number M obtained in this manner.  
         [0127]     It is assumed that also in the first word lexicon database  7022 , time and parameters are stored in correspondence with each other as shown in  FIG. 8 .  
         [0128]     In speech recognition apparatus  500 , time width D 2011  of frame shift for the second processing system is longer than time width D 201  of frame shift for the first processing system and, in addition, relation between the time width D 2011  and time width D 201  is determined such that each time point during variation with the longer time width D 2011  corresponds to or linked with a time point during variation with the shorter time width D 201 .  
         [0129]     By way of example, variation with time width D 201  may be in geometric series or in arithmetic series with respect to the variation with time width D 2011 , and in that case, the second standard patterns can be formed from the first standard patterns without necessitating any special interpolating operation as required in the sixth embodiment.  
         [0130]     Other configurations and operations of the speech recognition apparatus in accordance with the eighth embodiment, and therefore, description thereof will not be repeated.  
         [0131]     By the speech recognition apparatus in accordance with the eighth embodiment having such a configuration, in addition to the effects attained by speech recognition apparatus  400 , a further effect of alleviating burden of processing can be attained.  
         [0132]     Although the present invention has been described and illustrated in detail, it is clearly understood that the same is by way of illustration and example only and is not to be taken by way of limitation, the spirit and scope of the present invention being limited only by the terms of the appended claims.