Speech encoding method and apparatus

Abstract: An encoding apparatus in which an input speech signal is divided on the block basis and encoded in terms of the resulting block as a unit. The encoding apparatus includes an encoding unit 120 for performing CELP encoding having a noise codebook 121 is made up of a code vector generated on clipping said Gaussian noise and a codebook vector obtained by learning using the codebook vector generated by clipping the Gaussian noise as initial values. The encoding apparatus enables optimum encoding for a variety of speech configurations.

Description: This invention relates to a speech encoding method and apparatus in which an input speech signal is divided on the block basis and encoded in terms of the resulting block as a unit. There have hitherto been known a variety of encoding methods for encoding an audio signal (inclusive of speech and acoustic signals) for compression by exploiting statistic properties of the signals in the time domain and in the frequency domain and psychoacoustic characteristics of the human ear. The encoding method may roughly be classified into time-domain encoding, frequency domain encoding and analysis/synthesis encoding. Examples of the high-efficiency encoding of speech signals include sinusoidal analysis encoding, such as harmonic encoding, multi-band excitation (MBE) encoding, sub-band coding (SBC), linear predictive coding (LPC), discrete cosine transform (DCT), modified DCT (MDCT) and fast Fourier transform (FFT). Other examples of the high-efficiency encoding of speech signals include code excited linear prediction (CELP) encoding by optimum vector closed loop search employing a synthesis by analysis method. In the code excited linear prediction, as an example of the high-efficiency encoding of the speech signals, the encoding quality is influenced significantly by the properties of the encoded speech signals. For example, there are a variety of configurations of the speech such that it is difficult to achieve satisfactory encoding for all of the speech including consonants close to the noise, such as "sa, shi, su, se and so" or consonants having sharp rising portions (steep rising consonants) such as "pa, pi, pu, pe or po". It is therefore an object of the present invention to provide a speech encoding method and apparatus whereby the speech of various configurations can be encoded satisfactorily. The speech encoding method and apparatus of the present invention as claimed in claims 1-5 perform encoding in terms of blocks, obtained on dividing the input speech signals on the time axis, as a unit, and the time-domain waveform vector-quantized by a closed loop search of the optimum vector using a synthesis by analysis method, in which a codebook for vector quantization obtained on clipping the Gaussian noise with plural threshold values. That is, according to the present invention, a code vector obtained on vector clipping the Gaussian noise with plural different threshold values is used for performing vector quantization in order to cope with various speech configurations. The present invention will be more clearly understood from the following description, given by way of example only, with reference to the accompanying drawings in which: Fig.1 is a block diagram showing a basic structure of a speech signal encoding method and a speech signal encoding apparatus (encoder) for carrying out the encoding method according to the present invention. Fig.2 is a block diagram showing a basic structure of a speech signal decoding apparatus (decoder) which is a decoding apparatus (decoder) shown in Fig.1. Fig.3 is a block diagram showing a more specified structure of the speech signal encoder shown in Fig. 1. Fig.4 is a block diagram showing a more detailed structure of the speech decoder shown in Fig.2. Fig.5 is a block diagram showing a basic structure of an LPC quantizer. Fig.6 is a block diagram showing a more detailed structure of the LPC quantizer. Fig.7 is a block diagram showing a basic structure of the vector quantizer. Fig.8 is a block diagram showing a more detailed structure of the vector quantizer. Fig.9 is a block circuit diagram showing a detailed structure of CELP encoding portion (second encoding unit) of the speech signal encoder of the present invention. Fig.10 is a flow chart for illustrating the processing flow in an arrangement of Fig.9. Figs.11A and 11B illustrate the Gaussian noise after clipping at different threshold values. Fig.12 is a flowchart showing the processing flow at the time of generating the shape codebook by learning. Fig.13 is a block diagram showing a structure of a transmission side of a portable terminal employing a speech signal encoder embodying the present invention. Fig.14 is a block diagram showing a structure of a receiving side of the portable terminal employing the speech signal decoder which is a counterpart device of Fig.13. Fig.15 is a table showing a output data for the different bit rates in the speech signal encoder of the present invention. Referring to the drawings, preferred embodiments of the present invention will be explained in detail. In Fig.1, there is shown in a block diagram a basic structure of a speech signal encoder for carrying out the speech encoding method of the present invention. The speech signal encoder includes an inverse LPC filter 11, as means for finding short-term prediction residuals of input speech signals, and a sinusoidal analytic encoder 114 as means for finding sinusoidal analysis encoding parameters from the short-term prediction residuals. The speech signal encoder also includes a vector quantization unit 116 as means for performing perceptually weighted vector quantization on the sinusoidal analytic encoding parameters and a second encoding unit 120 as means for encoding the input speech signal by phase transmission waveform encoding. Fig.2 is a block diagram showing a basic structure of a speech signal decoding apparatus (decoder) which is a counterpart device of the encoding apparatus (decoder) shown in Fig.1, Fig.3 is a block diagram showing a more specified structure of the speech signal encoder shown in Fig.1 and Fig.4 is a block diagram showing a more detailed structure of the speech decoder shown in Fig.2. The structures of the block diagrams of Figs. 1 to 4 are explained. The basic concept of the speech signal encoder of Fig.1 is that the encoder has a first encoding unit 110 for finding short-term prediction residuals, such as linear prediction encoding (LPC) residuals, of the input speech signal for performing sinusoidal analysis encoding, such as harmonic coding, and a second encoding unit 120 for encoding the input speech signals by waveform coding exhibiting phase reproducibility, and that the first encoding units 110, 120 are used for encoding the voiced portion and unvoiced portion of the input signal, respectively. The first encoding unit 110 has a constitution of encoding the LPC residuals with sinusoidal analytic encoding such as harmonics encoding or multi-band encoding (MBE). The second encoding unit 120 has a constitution of code excitation linear prediction (CELP) employing vector quantization by a closed loop search for an optimum vector employing an analysis by synthesis method. In the embodiment, the speech signal supplied to the input terminal 101 is sent the inverse LPC filter 111 and an LPC analysis/quantization unit 113 of the first encoding unit 110. The LPC coefficient obtained from the LPC analysis/quantization unit 113 or the so-called α-parameter is sent to the inverse LPC filter 111 for taking out the linear prediction residuals (LPC residuals) of the input speech signals by the inverse LPC filter 111. From the LPC analysis/quantization unit 113, a quantization output of the linear spectral pairs (LSP) is taken out as later explained and sent to an output terminal 102. The LPC residuals from the inverse LPC filter 111 are sent to a sinusoidal analysis encoding unit 114. The sinusoidal analysis encoding unit 114 performs pitch detection, spectral envelope amplitude calculations and V/UV discrimination by a voiced (V)/ unvoiced (UV) discrimination unit 115. The spectral envelope amplitude data from the sinusoidal analysis encoding unit 114 are sent to the vector quantization unit 116. The codebook index from the vector quantization unit 116, as a vector quantization output of the spectral envelope, is sent via a switch 117 to an output terminal 103, while an output of the sinusoidal analysis encoding unit 114 is sent via a switch 118 to an output terminal 104. The V/UV discrimination output from the V/UV discrimination unit 115 is sent to an output terminal 105 and to the switches 117, 118 as switching control signals. For the voiced (V) signal, the index and the pitch are selected so as to be taken out at the output terminals 103, 104. In the present embodiment, the second encoding unit 120 of Fig.1 has a code excitation linear prediction (CELP) encoding configuration, and performs vector quantization of the time-domain waveform employing the closed-loop search by the analysis by synthesis method in which an output of a noise codebook 121 is synthesized by a weighted synthesis filter 122, the resulting weighted speech is sent to a subtractor 123 where an error between the weighted speech and the speech signal supplied to the input terminal 101 and thence passed through a perceptually weighted filter 125 is taken out and sent to a distance calculation circuit 124 in order to perform distance calculations and a vector which minimizes the error is searched by the noise codebook 121. This CELP encoding is used for encoding the unvoiced portion as described above. The codebook index as the UV data from the noise codebook 121 is taken out at an output terminal 107 via a switch 127 which is turned on when the results of V/UV discrimination from the V/UV discrimination unit 115 indicates an unvoiced (UV) sound. Fig.2 is a block diagram showing the basic structure of a speech signal decoder, as a counterpart device of the speech signal encoder of Fig.1, for carrying out the speech decoding method according to the present invention. Referring to Fig.2, a codebook index as a quantization output of the linear spectral pairs (LSPs) from the output terminal 102 of Fig.1 is supplied to an input terminal 202. Outputs of the output terminals 103, 104 and 105 of Fig. 1, that is the index data, pitch and the V/UV discrimination output as the envelope quantization outputs, are supplied to input terminals 203 to 205, respectively. The index data as data for the unvoiced data are supplied from the output terminal 107 of Fig.1 to an input terminal 207. The index as the quantization output of the input terminal 203 is sent to an inverse vector quantization unit 212 for inverse vector quantization to find a spectral envelope of the LPC residues which is sent to a voiced speech synthesizer 211. The voiced speech synthesizer 211 synthesizes the linear prediction encoding (LPC) residuals of the voiced speech portion by sinusoidal synthesis. The voiced speech synthesizer 211 is also fed with the pitch and the V/UV discrimination output from the input terminals 204, 205. The LPC residuals of the voiced speech from the voiced speech synthesis unit 211 are sent to an LPC synthesis filter 214. The index data of the UV data from the input terminal 207 is sent to an unvoiced sound synthesis unit 220 where reference is had to the noise codebook for taking out the LPC residuals of the unvoiced portion. These LPC residuals are also sent to the LPC synthesis filter 214. In the LPC synthesis filter 214, the LPC residuals of the voiced portion and the LPC residuals of the unvoiced portion are processed by LPC synthesis. Alternatively, the LPC residuals of the voided portion and the LPC residuals of the unvoiced portion summed together may be processed with LPC synthesis. The LSP index data from the input terminal 202 is sent to the LPC parameter reproducing unit 213 where α-parameters of the LPC are taken out and sent to the LPC synthesis filter 214. The speech signals synthesized by the LPC synthesis filter 214 are taken out at an output terminal 201. Referring to Fig.3, a more detailed structure of a speech signal encoder shown in Fig. 1 is now explained. In Fig.3, the parts or components similar to those shown in Fig. 1 are denoted by the same reference numerals. In the speech signal encoder shown in Fig.3, the speech signals supplied to the input terminal 101 are filtered by a high-pass filter 109 for removing signals of an unneeded range and thence supplied to an LPC analysis circuit 132 of the LPC analysis/quantization unit 113 and to the inverse LPC filter 111. The LPC analysis circuit 132 of the LPC analysis/ quantization unit 113 applies a Hamming window, with a length of the input signal waveform on the order of 256 samples as a block, and finds a linear prediction coefficient, that is a so-called α-parameter, by the self-correlation method. The framing interval as a data outputting unit is set to approximately 160 samples. If the sampling frequency fs is 8 kHz, for example, one-frame interval is 20 msec for 160 samples. The α-parameter from the LPC analysis circuit 132 is sent to an α-LSP conversion circuit 133 for conversion into line spectra pair (LSP) parameters. This converts the α-parameter, as found by direct type filter coefficient, into for example, ten, that is five pairs of the LSP parameters. This conversion is carried out by, for example, the Newton-Rhapson method. The reason the α-parameters are converted into the LSP parameters is that the LSP parameter is superior in interpolation characteristics to the α-parameters. The LSP parameters from the α-LSP conversion circuit 133 are matrix- or vector quantized by the LSP quantizer 134. It is possible to take a frame-to-frame difference prior to vector quantization, or to collect plural frames in order to perform matrix quantization. In the present case, two frames (20 msec) of the LSP parameters, calculated every 20 msec, are collected and processed with matrix quantization and vector quantization. The quantized output of the quantizer 134, that is the index data of the LSP quantization, are taken out at a terminal 102, while the quantized LSP vector is sent to an LSP interpolation circuit 136. The LSP interpolation circuit 136 interpolates the LSP vectors, quantized every 20 msec or 40 msec, in order to provide an eight-fold rate. That is, the LSP vector is updated every 2.5 msec. The reason is that, if the residual waveform is processed with the analysis/synthesis by the harmonic encoding/decoding method, the envelope of the synthetic waveform presents an extremely sooth waveform, so that, if the LPC coefficients are changed abruptly every 20 msec, a foreign noise is likely to be produced. That is, if the LPC coefficient is changed gradually every 2.5 msec, such foreign noise may be prevented from occurrence. For inverse filtering of the input speech using the interpolated LSP vectors produced every 2.5 msec, the LSP parameters are converted by an LSP to α conversion circuit 137 into α-parameters as coefficients of e.g., ten-order direct type filter. An output of the LSP to α conversion circuit 137 is sent to the LPC inverse filter circuit 111 which then performs inverse filtering for producing a smooth output using an α-parameter updated every 2.5 msec. An output of the inverse LPC filter 111 is sent to an orthogonal transform circuit 145, such as a DCT circuit, of the sinusoidal analysis encoding unit 114, such as a harmonic encoding circuit. The α-parameter from the LPC analysis circuit 132 of the LPC analysis/quantization unit 113 is sent to a perceptual weighting filter calculating circuit 139 where data for perceptual weighting is found. These weighting data are sent to a perceptual weighting vector quantizer 116, perceptual weighting filter 125 of the second encoding unit 120 and the perceptual weighted synthesis filter 122. The sinusoidal analysis encoding unit 114 of the harmonic encoding circuit analyzes the output of the inverse LPC filter 111 by a method of harmonic encoding. That is, pitch detection, calculations of the amplitudes Am of the respective harmonics and voiced (V)/ unvoiced (UV) discrimination are carried out and the numbers of the amplitudes Am or the envelopes of the respective harmonics, varied with the pitch, are made constant by dimensional conversion. In an illustrative example of the sinusoidal analysis encoding unit 114 shown in Fig.3, commonplace harmonic encoding is used. In particular, in multi-band excitation (MBE) encoding, it is assumed in modeling that voiced portions and unvoiced portions are present in the frequency area or band at the same time point (in the same block or frame). In other harmonic encoding techniques, it is uniquely judged whether the speech in one block or in one frame is voiced or unvoiced. In the following description, a given frame is judged to be UV if the totality of the band is UV, insofar as the MBE encoding is concerned. The open-loop pitch search unit 141 and the zero-crossing counter 142 of the sinusoidal analysis encoding unit 114 of Fig.3 is fed with the input speech signal from the input terminal 101 and with the signal from the high-pass filter (HPF) 109, respectively. The orthogonal transform circuit 145 of the sinusoidal analysis encoding unit 114 is supplied with LPC residuals or linear prediction residuals from the inverse LPC filter 111. The open loop pitch search unit 141 takes the LPC residuals of the input signals to perform relatively rough pitch search by open loop. The extracted rough pitch data is sent to a fine pitch search unit 146 by closed loop as later explained. From the open loop pitch search unit 141, the maximum value of the normalized self correlation r(p), obtained by normalizing the maximum value of the self-correlation of the LPC residuals along with the rough pitch data, are taken out along with the rough pitch data so as to be sent to the V/UV discrimination unit 115. The orthogonal transform circuit 145 performs orthogonal transform, such as discrete Fourier transform (DFT) for converting the LPC residuals on the time axis into spectral amplitude data on the frequency axis. An output of the orthogonal transform circuit 145 is sent to the fine pitch search unit 146 and a spectral evaluation unit 148 for evaluating the spectral amplitude or envelope. The fine pitch search unit 146 is fed with relatively rough pitch data extracted by the open loop pitch search unit 141 and with frequency-domain data obtained by DFT by the orthogonal transform unit 145. The fine pitch search unit 146 swings the pitch data by ± several samples, at a rate of 0.2 to 0.5, centered about the rough pitch value data, in order to arrive ultimately at the value of the fine pitch data having an optimum decimal point (floating point). The analysis by synthesis method is used as the fine search technique for selecting a pitch so that the power spectrum will be closest to the power spectrum of the original sound. Pitch data from the closed-loop fine pitch search unit 146 is sent to an output terminal 104 via a switch 118. In the spectral evaluation unit 148, the amplitude of each harmonics and the spectral envelope as the sum of the harmonics are evaluated based on the spectral amplitude and the pitch as the orthogonal transform output of the LPC residuals and sent to the fine pitch search unit 146, V/UV discrimination unit 115 and the perceptually weighted vector quantization unit 116. The V/UV discrimination unit 115 discriminates V/UV of a frame based on an output of the orthogonal transform circuit 145, an optimum pitch from the fine pitch search unit 146, spectral amplitude data from the spectral evaluation unit 148, maximum value of the normalized self-correlation r(p) from the open loop pitch search unit 141 and the zero-crossing count value from the zero-crossing counter 142. In addition, the boundary position of the band-based V/UV discrimination for the MBE may also be used as a condition for V/UV discrimination. A discrimination output of the V/UV discrimination unit 115 is taken out at an output terminal 105. An output unit of the spectrum evaluation unit 148 or an input unit of the vector quantization unit 116 is provided with a data number conversion unit (a unit performing a sort of sampling rate conversion). The data number conversion unit is used for setting the amplitude data |Am| of an envelope taking into account the fact that the number of bands split on the frequency axis and the number of data differ with the pitch. That is, if the effective band is up to 3400 kHz, the effective band can be split into 8 to 63 bands depending on the pitch. The number of mMX + 1 of the amplitude data |Am|, obtained from band to band, is changed in a range from 8 to 63. Thus the data number conversion unit converts the amplitude data of the variable number mMx + 1 to a pre-set number M of data, such as 44 data. The amplitude data or envelope data of the pre-set number M, such as 44, from the data number conversion unit, provided at an output unit of the spectral evaluation unit 148 or at an input unit of the vector quantization unit 116, are collected in terms of a pre-set number of data, such as 44 data, as units, by the vector quantization unit 116, by way of performing weighted vector quantization. This weight is supplied by an output of the perceptual weighting filter calculation circuit 139. The index of the envelope from the vector quantizer 116 is taken out by a switch 117 at an output terminal 103. Prior to weighted vector quantization, it is advisable to take inter-frame difference using a suitable leakage coefficient for a vector made up of a pre-set number of data. The second encoding unit 120 is explained. The second encoding unit 120 has a so-called CELP encoding structure and is used in particular for encoding the unvoiced portion of the input speech signal. In the CELP encoding structure for the unvoiced portion of the input speech signal, a noise output, corresponding to the LPC residuals of the unvoiced sound as a representative value output of the noise codebook, or a so-called stochastic codebook 121, is sent via a gain control circuit 126 to a perceptually weighted synthesis filter 122. The weighted synthesis filter 122 LPC synthesizes the input noise and sends the produced weighted unvoiced signal to the subtractor 123. The subtractor 123 is fed with a signal supplied from the input terminal 101 via an high-pass filter (HPF) 109 and perceptually weighted by a perceptual weighting filter 125. The difference or error between the signal and the signal from the synthesis filter 122 is taken out. Meanwhile, a zero input response of the perceptually weighted synthesis filter is previously subtracted from an output of the perceptual weighting filter output 125. This error is fed to a distance calculation circuit 124 for calculating the distance. A representative vector value which will minimize the error is searched in the noise codebook 121. The above is the summary of the vector quantization of the time-domain waveform employing the closed-loop search in turn employing the analysis by synthesis method. As data for the unvoiced (UV) portion from the second encoder 120 employing the CELP coding structure, the shape index of the codebook from the noise codebook 121 and the gain index of the codebook from the gain circuit 126 are taken out. The shape index, which is the UV data from the noise codebook 121, and the gain index, which is the UV data of the gain circuit 126, are sent via a switch 127g to an output terminal 107g. These switches 127s, 127g and the switches 117, 118 are turned on and off depending on the results of V/UV decision from the V/UV discrimination unit 115. Specifically, the switches 117, 118 are turned on, if the results of V/UV discrimination of the speech signal of the frame currently transmitted indicates voiced (V), while the switches 127s, 127g are turned on if the speech signal of the frame currently transmitted is unvoiced (UV). Fig.4 shows a more detailed structure of a speech signal decoder shown in Fig.2. In Fig.4, the same numerals are used to denote the opponents shown in Fig.2. In Fig.4, a vector quantization output of the LSP corresponding to the output terminal 102 of Figs. 1 and 3, that is the codebook index, is supplied to an input terminal 202. The LSP index is sent to he inverse vector quantizer 231 of the LSP for the LPC parameter reproducing unit 213 so as to be inverse vector quantized to line spectral pair (LSP) data which are then supplied to LSP interpolation circuits 232, 233 for interpolation. The resulting interpolated data is converted by the LSP to α conversion circuits 234, 235 to α parameters which are sent to the LPC synthesis filter 214. The LSP interpolation circuit 232 and the LSP to α conversion circuit 234 are designed for voiced (V) sound, while the LSP interpolation circuit 233 and the LSP to α conversion circuit 235 are designed for unvoiced (UV) sound. The LPC synthesis filter 214 separates the LPC synthesis filter 236 of the voiced speech portion from the LPC synthesis filter 237 of the unvoiced speech portion. That is, LPC coefficient interpolation is carried out independently for the voiced speech portion and the unvoiced speech portion for prohibiting ill effects which might otherwise be produced in the transition portion from the voiced speech portion to the unvoiced speech portion or vice versa by interpolation of the LSPs of totally different properties. To an input terminal 203 of Fig.4 is supplied code index data corresponding to the weighted vector quantized spectra envelope Am corresponding to the output of the terminal 103 of the encoder of Figs.1 and 3. To an input terminal 204 is supplied pitch data from the terminal 104 of Figs. 1 and 3 and, to an input terminal 205 is supplied V/UV discrimination data from the terminal 105 of Figs. 1 and 3. The vector-quantized index data of the spectral envelope Am from the input terminal 203 is sent to an inverse vector quantizer 212 for inverse vector quantization where an inverse conversion with respect to the data number conversion is carried out. The resulting spectral envelope data is sent to a sinusoidal synthesis circuit 215. If the inter-frame difference is found prior to vector quantization of the spectrum during encoding, inter-frame difference is decoded after inverse vector quantization for producing the spectral envelope data. The sinusoidal synthesis circuit 215 is fed with the pitch from the input terminal 204 and the V/UV discrimination data from the input terminal 205. From the sinusoidal synthesis circuit 215, LPC residual data corresponding to the output of the LPC inverse filter 111 shown in Figs.1 and 3 are taken out and sent to an adder 218. The envelop data of the inverse vector quantizer 212 and the pitch and the V/UV discrimination data from the input terminals 204, 205 are sent to a noise synthesis circuit 216 for noise addition for the voiced portion (V). An output of the noise synthesis circuit 216 is sent to an adder 218 via a weighted overlap-add circuit 217. Specifically, the noise taking into account the fact that, if the excitation as an input to the LPC synthesis filter of the voiced sound is produced by sine wave synthesis, stuffed feeling is produced in the low-pitch sound such as male speech, and the sound quality is abruptly changed between the voiced sound and the unvoiced sound thus producing an unnatural hearing feeling, is added to the voiced portion of the LPC residual signals. Such noise takes into account the parameters concerned with speech encoding data, such as pitch, amplitudes of the spectral envelope, maximum amplitude in a frame or the residual signal level, in connection with the LPC synthesis filter input of the voiced speech portion, that is excitation. An add output of the adder 218 is sent to a synthesis filter 236 for the voiced sound of the LPC synthesis filter 214 where LPC synthesis is carried out to form time waveform data which then is filtered by a post-filter 238v for the voiced speech and sent to the adder 239. The shape index and the gain index, as UV data from the output terminals 107s and 107g of Fig.3, are supplied to the input terminals 207s and 207g of Fig.4, and thence supplied to the unvoiced speech synthesis unit 220. The shape index from the terminal 207s is sent to the noise codebook 221 of the unvoiced speech synthesis unit 220, while the gain index from the terminal 207g is sent to the gain circuit 222. The representative value output read out from the noise codebook 221 is a noise signal component corresponding to the LPC residuals of the unvoiced speech. This becomes a pre-set gain amplitude in the gain circuit 222 and is sent to a windowing circuit 223 so as to be windowed for smoothing junction to the voiced speech portion. An output of the windowing circuit 223 is sent to a synthesis filter 237 for the unvoiced (UV) speech of the LPC synthesis filter 214. The data sent to the synthesis filter 237 is processed with LPC synthesis to become time waveform data for the unvoiced portion. The time waveform data of the unvoiced portion is filtered by a post-filter for the unvoiced portion before being sent to an adder 239. In the adder 239, the time waveform signal from the post-filter for the voiced speech 238v and the time waveform data for the unvoiced speech portion from the post-filter 238u for the unvoiced speech are added to each other and the resulting sum data is taken out at the output terminal 201. The above-described speech signal encoder can output data of different bit rates depending on the demanded sound quality. That is, the output data can be outputted with variable bit rates. For example, if the low bit rate is 2 kbps and the high bit rate is 6 kbps, the output data is data of the bit rates having the bit rates shown in Fig.15. The pitch data from the output terminal 104 is outputted at al times at a bit rate of 8 bits/ 20 msec for the voiced speech, with the V/UV discrimination output from the output terminal 105 being at all times 1 bit/ 20 msec. The index for LSP quantization, outputted from the output terminal 102, is switched between 32 bits/ 40 msec and 48 bits/ 40 msec. On the other hand, the index during the voiced speech (V) outputted by the output terminal 103 is switched between 15 bits/ 20 msec and 87 bits/ 20 msec. The index for the unvoiced (UV) outputted from the output terminals 107s and 107g is switched between 11 bits/ 10 msec and 23 bits/ 5 msec. The output data for the voiced sound (UV) is 40 bits/ 20 msec for 2 kbps and 120 kbps/ 20 msec for 6 kbps. On the other hand, the output data for the voiced sound (UV) is 39 bits/ 20 msec for 2 kbps and 117 kbps/ 20 msec for 6 kbps. The index for LSP quantization, the index for voiced speech (V) and the index for the unvoiced speech (UV) are explained later on in connection with the arrangement of pertinent portions. Referring to Figs.5 and 6, matrix quantization and vector quantization in the LSP quantizer 134 are explained in detail. The α-parameter from the LPC analysis circuit 132 is sent to an α-LSP circuit 133 for conversion to LSP parameters. If the P-order LPC analysis is performed in a LPC analysis circuit 132, P α-parameters are calculated. These P α-parameters are converted into LSP parameters which are held in a buffer 610. The buffer 610 outputs 2 frames of LSP parameters. The two frames of the LSP parameters are matrix-quantized by a matrix quantizer 620 made up of a first matrix quantizer 620 1 and a second matrix quantizer 620 2 . The two frames of the LSP parameters are matrix-quantized in a first matrix quantizer 620 1 and the resulting quantization error is further matrix-quantized in the second matrix quantizer 620 2 . The matrix quantization exploits correlation in both the time axis and in the frequency axis. The quantization error for two frames from the matrix quantizer 620 2 enters a vector quantization unit 640 made up of a first vector quantizer 640 1 and a second vector quantizer 640 2 . The first vector quantizer 640 1 is made up of two vector quantization portions 650, 660, while the second vector quantizer 640 2 is made up of two vector quantization portions 670, 680. The quantization error from the matrix quantization unit 620 is quantized on the frame basis by the by the vector quantization portions 650, 660 of the first vector quantizer 640 1 . The resulting quantization error vector is further vector-quantized by the vector quantization portions 670, 680 of the second vector quantizer 640 2 . The above described vector quantization exploits correlation along the frequency axis. The matrix quantization unit 620, executing the matrix quantization as described above, includes at least a first matrix quantizer 620 1 for performing first matrix quantization step and a second matrix quantizer 620 2 for performing second matrix quantization step for matrix quantizing the quantization error produced by the first matrix quantization. The vector quantization unit 640, executing the vector quantization as described above, includes at least a first vector quantizer 640 1 for performing first vector quantization step and a second vector quantizer 640 2 for performing second matrix quantization step for matrix quantizing the quantization error produced by the first vector quantization. The matrix quantization and the vector quantization will now be explained in detail. The LSP parameters for two frames, stored in the buffer 600, that is a 10×2 matrix, is sent to the first matrix quantizer 620 1 . The first matrix quantizer 620 1 sends LSP parameters for two frames via LSP parameter adder 621 to a weighted distance calculating unit 623 for finding the weighted distance of the minimum value. The distortion measure d MQ1 during codebook search by the first matrix quantizer 620 1 is given by the equation (1): where X 1 is the LSP parameter and X 1 ' is the quantization value, with t and i being the numbers of the P-dimension. The weight w(t, i), in which weight limitation in the frequency axis and in the time axis is not taken into account, is given by the equation (2): w ( t , i ) = 1 x ( t,i +1) - x ( t,i ) + 1 x ( t,i ) - x ( t,i -1) where x(t, 0) = 0, x(t, p+1) = π regardless of t. The weight of the equation (2) is also used for downstream side matrix quantization and vector quantization. The calculated weighted distance is sent to a matrix quantizer MQ 1 622 for matrix quantization. An 8-bit index outputted by this matrix quantization is sent to a signal switcher 690. The quantization value by matrix quantization is subtracted in an adder 621 from the LSP parameters for two frames. A weighted distance calculating unit 623 sequentially calculates the weighted distance every two frames so that matrix quantization is carried out in the matrix quantization unit 622. Also, a quantization value minimizing the weighted distance is selected. An output of the adder 621 is sent to an adder 631 of the second matrix quantizer 620 2 . Similarly to the first matrix quantizer 620 1 , the second matrix quantizer 620 2 performs matrix quantization. An output of the adder 621 is sent via adder 631 to a weighted distance calculation unit 633 where the minimum weighted distance is calculated. The distortion measure d MQ2 during the codebook search by the second matrix quantizer 620 2 is given by the equation (3): where X 2 and X 2 ' are the quantization error and the quantization value from the first matrix quantizer 620 1 , respectively. The weighted distance is sent to a matrix quantization unit (MQ 2 ) 632 for matrix quantization. An 8-bit index, outputted by matrix quantization, is subtracted at the adder 631 from the two-frame quantization error. The weighted distance calculation unit 633 sequentially calculates the weighted distance using the output of the adder 631. The quantization value minimizing the weighted distance is selected. An output of the adder 631 is sent to the adders 651, 661 of the first vector quantizer 640 1 frame by frame. The first vector quantizer 640 1 performs vector quantization frame by frame. An output of the adder 631 is sent frame by frame to each of weighted distance calculating units 653, 663 via adders 651, 661 for calculating the minimum weighted distance. The difference between the quantization error X 2 and the quantization error X 2 ' is a matrix of (10 × 2). If the difference is represented as X 2 - X 2 ' = [ x 3-1 , x 3-2 ], the distortion measures d VQ1 , d VQ2 during codebook search by the vector quantization units 652, 662 of the first vector quantizer 640 1 are given by the equations (4) and (5): The weighted distance is sent to a vector quantization VQ 1 652 and a vector quantization unit VQ 2 662 for vector quantization. Each 8-bit index outputted by this vector quantization is sent to the signal switcher 690. The quantization value is subtracted by the adders 651, 661 from the input two-frame quantization error vector. The weighted distance calculating units 653, 663 sequentially calculate the weighted distance, using the outputs of the adders 651, 661, for selecting the quantization value minimizing the weighted distance. Te outputs of the adders 651, 661 are sent to adders 671, 681 of the second vector quantizer 640 2 . The distortion measure d VQ3 , d VQ4 during codebook searching by the vector quantizers 672, 682 of the second vector quantizer 640 2 , for x 4-1 = x 3-1 - x 3-1 ' x 4-2 = x 3-2 - x 3-2 ' are given by the equations (6) and (7): These weighted distances are sent to the vector quantizer (VQ 3 ) 672 and to the vector quantizer (VQ 4 ) 682 for vector quantization. The 8-bit output index data from vector quantization are subtracted by the adders 671, 681 from the input quantization error vector for two frames. The weighted distance calculating units 673, 683 sequentially calculate the weighted distances using the outputs of the adders 671, 681 for selecting the quantization value minimizing the weighted distances. During codebook learning, learning is performed by the general Lloyd algorithm based on the respective distortion measures. The distortion measures during codebook searching and during learning may be of different values. The 8-bit index data from the matrix quantization units 622, 632 and the vector quantization units 652, 662, 672 and 682 are switched by the signal switcher 690 and outputted at an output terminal 691. Specifically, for a low-bit rate, outputs of the first matrix quantizer 620 1 carrying out the first matrix quantization step, second matrix quantizer 620 2 carrying out the second matrix quantization step and the first vector quantizer 640 1 carrying out the first vector quantization step are taken out, whereas, for a high bit rate, the output for the low bit rate is summed to an output of the second vector quantizer 640 2 carrying out the second vector quantization step and the resulting sum is taken out. This outputs an index of 32 bits/ 40 msec and an index of 48 bits/ 40 msec for 2 kbps and 6 kbps, respectively. The matrix quantization unit 620 and the vector quantization unit 640 perform weighting limited in the frequency axis and/or the time axis in conformity to characteristics of the parameters representing the LPC coefficients. The weighting limited in the frequency axis in conformity to characteristics of the LSP parameters is first explained. If the number of orders P = 10, the LSP parameters X(i) are grouped into L 1 = {X(i) |1 ≤ i ≤ 2} L 2 = {X(i) |3 ≤ i ≤ 6} L 3 = {X(i) |7 ≤ i ≤ 10} for three ranges of low, mid and high ranges. If the weighting of the groups L 1 , L 2 and L 3 is 1/4, ½ and 1/4, the weighting limited only in the frequency axis is given by the equations (8), (9) and (10) The weighting of the respective LSP parameters is performed in each group only and such weight is limited by the weighting for each group. Looking in the time axis direction, the sum total of the respective frames is necessarily 1, so that limitation in the time axis direction is frame-based. The weight limited only in the time axis direction is given by the equation (11): where 1 ≤ i ≤ 10 and 0 ≤ t ≤ 1. By this equation (11), weighting not limited in the frequency axis direction is carried out between two frames with the frame numbers of t = 0 and t = 1. This weighting limited only in the time axis direction is carried out between two frames processed with matrix quantization. During learning, the totality of frames used as learning data, having the total number T, is weighted in accordance with the equation (12): where 1 ≤ i ≤ 10 and 0 ≤ t ≤ T The weighting limited in the frequency axis direction and in the time axis direction is explained. If the number of orders P = 10, the LSP parameters X(i, t) are grouped into L 1 = {X(i, t) | 1 ≤ i ≤ 2, 0 ≤ t ≤ 1} L 2 = {X(i, t)| 3 ≤ i ≤ 6, 0 ≤ t ≤ 1} L 3 = {X(i, t)| 7 ≤ i ≤ 10, 0 ≤ t ≤ 1} for three ranges of low, mid and high ranges. If the weighting of the groups L 1 , L 2 and L 3 is 1/4, ½ and 1/4, the weighting limited only in the frequency axis is given by the equations (13), (14) and (15): By these equations (13) to (15), weighting limited every three frames in the frequency axis direction and across two frames processed with matrix quantization is carried out. This is effective during codebook search and during learning. During learning, weighting is for the totality of frames of the entire data. The LSP parameters X(i, t) are grouped into L 1 = {X(i, t)| 1 ≤ i ≤ 2, 0 ≤ t ≤ T} L 2 = {X(i, t)| 3 ≤ i ≤ 6, 0 ≤ t ≤ T} L 3 = {X(i, t)| 7 ≤ i ≤ 10, 0 ≤ t ≤ T} for low, mid and high ranges. If the weighting of the groups L 1 , L 2 and L 3 is 1/4, ½ and 1/4, the weighting for the groups L 1 , L 2 and L 3 , limited only in the frequency axis, is given by the equations (16), (17) and (18): By these equations (16) to (18), weighting can be performed for three ranges in the frequency axis direction and across the totality of frames in the time axis direction. In addition, the matrix quantization unit 620 and the vector quantization unit 640 perform weighting depending on the magnitude of changes in the LSP parameters. In V to UV or UV to V transition regions, which represent minority frames among the totality of speech frames, the LSP parameters are changed significantly due to difference in the frequency response between consonants and vowels. Therefore, the weighting shown by the equation (19) may be multiplied by the weighting W'(i, t) for weighting placing emphasis on the transition regions. The following equation (20): may be used in place of the equation (19). Thus the LSP quantization unit 134 executes two-stage matrix quantization and two-stage vector quantization to render the number of bits of the output index variable. The basic structure of the vector quantization unit 116 is shown in Fig.7, while a more detailed structure of the vector quantization unit 116 shown in Fig.7 is shown in Fig.8. An illustrative structure of weighted vector quantization for the spectral envelope Am in the vector quantization unit 116 is now explained. First, in the speech signal encoding device shown in Fig.3, an illustrative arrangement for data number conversion for providing a constant number of data of the amplitude of the spectral envelope on an output side of the spectral evaluating unit 148 or on an input side of the vector quantization unit 116 is explained. A variety of methods may be conceived for such data number conversion. In the present embodiment, dummy data interpolating the values from the last data in a block to the first data in the block or other pre-set data such as data repeating the last data or the first data in a block are appended to the amplitude data of one block of an effective band on the frequency axis for enhancing the number of data to N F , amplitude data equal in number to Os times, such as eight times, are found by Os-fold, such as eight-fold oversampling of the limited bandwidth type by, for example, an FIR filter. The ((mMx + 1) × Os amplitude data are linearly interpolated for expansion to a larger N M number, such as 2048. This N M data is sub-sampled for conversion to the above-mentioned pres-set number M of data, such as 44 data. In effect, only data necessary for formulating M data ultimately required is calculated by oversampling and linear interpolation without finding the above-mentioned N M data. The vector quantization unit 116 for carrying out weighted vector quantization of Fig.7 at least includes a first vector quantization unit 500 for performing the first vector quantization step and a second vector quantization unit 510 for carrying out the second vector quantization step for quantizing the quantization error vector produced during the first vector quantization by the first vector quantization unit 500. This first vector quantization unit 500 is a so-called first-stage vector quantization unit, while the second vector quantization unit 510 is a so-called second-stage vector quantization unit. An output vector x of the spectral evaluation unit 148, that is envelope data having a pre-set number M, enters an input terminal 501 of the first vector quantization unit 500. This output vector x is quantized with weighted vector quantization by the vector quantization unit 502. Thus a shape index outputted by the vector quantization unit 502 is outputted at an output terminal 503, while a quantized value x 0 ' is outputted at an output terminal 504 and sent to adders 505, 513. The adder 505 subtracts the quantized value x 0 ' from the source vector x to give a multi-order quantization error vector y . The quantization error vector y is sent to a vector quantization unit 511 in the second vector quantization unit 510. This second vector quantization unit 511 is made up of plural vector quantization units, or two vector quantizers 511 1 , 511 2 in Fig.7. The quantization error vector y is dimensionally split so as to be quantized by weighted vector quantization in the two vector quantizers 511 1 , 511 2 . The shape index outputted by these vector quantizers 511 1 , 511 2 is outputted at output terminals 512 1 , 512 2 while the quantized values y 1 ', y 2 ' are connected in the dimensional direction and sent to an adder 513. The adder 513 adds the quantized values y 1 ', y 2 ' to the quantized value x 0 ' to generate a quantized value x 1 ' which is outputted at an output terminal 514. Thus, for the low bit rate, an output of the first vector quantization step by the first vector quantization unit 500 is taken out, whereas, for the high bit rate, an output of the first vector quantization step and an output of the second quantization step by the second quantization unit 510 are outputted. Specifically, the vector quantizer 502 in the first vector quantization unit 500 in the vector quantization section 116 is of an L-order, such as 44-order two-stage structure, as shown in Fig.8. That is, the sum of the output vectors of the 44-order vector quantization codebook with the codebook size of 32, multiplied with a gain g i , is used as a quantized value x 0 ' of the 44-order spectral envelope vector x. Thus, as shown in Fig.8, the two codebooks are CB0 and CB1, while the output vectors are s 1i , s 1j , where 0 ≤ i and j ≤ 31. On the other hand, an output of the gain codebook CB g is g 1 , where 0 ≤ 1 ≤ 31, where g 1 is a scalar. An ultimate output x 0 ' is g 1 ( s 1i + s 1j ). The spectral envelope Am obtained by the above MBE analysis of the LPC residuals and converted into a pre-set order is x It is crucial how efficiently x is to be quantized. The quantization error energy E is defined by E = W {H x - Hg1(( s 0i + s 1j )} 2 = WH { x - { x - g 1 ( s 0i + s 1j )} 2 where H denotes characteristics on the frequency axis of the LPC synthesis filter and W a matrix for weighting for representing characteristics for perceptual weighting on the frequency axis. If the α-parameter by the results of LPC analysis of the current frame is denoted as α i (1 ≤ i ≤ P), the values of the L-order, for example, 44-order corresponding points, are sampled from the frequency response of the equation (22): For calculations, 0s are stuffed next to a string of 1, α 1 , α 2 , ...α P to give a string of 1, α 1 , α 2 , ... α P , 0, 0, ..., 0 to give e.g., 256-point data. Then, by 256-point FFT, (r e 2 + im 2 ) 1/2 are calculated for point associated with a range from 0 to π and the reciprocals of the results are found. These reciprocals are sub-sampled to L points, such as 44 points, and a matrix is formed having these L points as diagonal elements: A perceptually weighted matrix W is given by the equation (23): where α i is the result of the LPC analysis, and λ a, λ b are constants, such that λ a = 0.4 and λ b = 0.9. The matrix W may be calculated from the frequency response of the above equation (23). For example, FFT is done on 256-point data of 1, α1λb, α2λ1b 2 , ... αpλb p , 0, 0, ..., 0 to find (r e 2 [i] + Im 2 [i]) 1/2 for a domain from 0 to π, where 0 ≤ i ≤ 128. The frequency response of the denominator is found by 256-point FFT for a domain from 0 to π for 1, α1λa, α2λa 2 , ..., αpλa p , 0, 0, ..., 0 at 128 points to find (re' 2 [i] + im' 2 [i]) 1/2 , where 0 ≤ i ≤ 128. The frequency response of the equation 23 may be found by w 0 [ i ] = re 2 [ i ] + im 2 [ i ] re' 2 [ i ] + im' 2 [ i ] where 0 ≤ i ≤ 128. This is found for each associated point of, for example, the 44-order vector, by the following method. More precisely, linear interpolation should be used. However, in the following example, the closest point is used instead. That is, ω[i] = ω0 [nint{128i /L)], where 1 ≤ i ≤ L. In the equation nint(X) is a function which returns a value closest to X. As for H , h(1), h(2), ...h(L) are found by a similar method. That is, As another example, H(z)W(z) is first found and the frequency response is then found for decreasing the number of times of FFT. That is, the denominator of the equation (25): is expanded to 256-point data, for example, is produced by using a string of 1, β 1 , β 2 , ..., β 2p , 0, 0, ..., 0. Then, 256-point FFT is done, with the frequency response of the amplitude being rms [ i ] = re'' 2 [ i ] + im'' 2 [ i ] where 0 ≤ i ≤ 128. From this, wh 0 [ i ] = re 2 [ i ] + im 2 [ i ] re'' 2 [ i ] + im'' 2 [ i ] where 0 ≤ i ≤ 128. This is found for each of corresponding points of the L-dimensional vector. If the number of points of the FFT is small, linear interpolation should be used. However, the closest value is herein is found by: wh [ i ] = wh 0 [ n int ( 128 L · i )] where 1 ≤ i ≤ L. If a matrix having these as diagonal elements is W ', The equation (26) represents the same matrix as the equation (24). Alternatively, | H(exp(jω))W(exp(jω))| may directly be found from the equation (25) with respect to ω = i/Lλ so as to be used for wh[i]. Still alternatively, an impulse response of the equation (25) is found a suitable length, such as for 64 points, and FFTed to find amplitude frequency characteristics which may then be used for wh[i]. Rewriting the equation (21) using this matrix, that is the frequency response of the weighted synthesis filter, we obtain the equation (21): E = W'( x - g 1 (( s 0i + s 1j )) 2 The method for learning the shape codebook and the gain codebook is explained. The expected value of the distortion is minimized for all frames k for which a code vector s 0 c is selected for CB0. If there are M such frames, it suffices if is minimized. In the equation (28), W k ', X k , g k and s ik denote the weighting for the k'th frame, an input to the k'th frame, the gain of the k'th frame and an output of the codebook CB1 for the k'th frame, respectively. For minimizing the equation (28), Hence, so that where denotes an inverse matrix and W k 'T denotes a transposed matrix of W k '. Next, gain optimization is considered. The expected value of the distortion concerning the k'th frame selecting the code word gc of the gain is given by: Solving we obtain and The above equations (31) and (32) give optimum centroid conditions for the shape s 0i , s 1i , and the gain gi for 0 ≤ i ≤ 31, that is an optimum decoder output. Meanwhile, s 1i may be found in the same way as for s 0i . The optimum encoding condition, that is the nearest neighbor condition, is considered. The above equation (27) for finding the distortion measure, that is s0i and s 1i minimizing the equation E = ∥ W ' (X - gc( s 1i + s 1j ))∥ 2 , are found each time the input x and the weight matrix W' are given, that is on the frame-by-frame basis. Intrinsically, E is found on the round robin fashion for all combinations of gl (0 ≤1 ≤ 31), s 0i (0 ≤ i ≤ 31) and s1j (0 ≤ i ≤ 31), that is 32×32×32 = 32768, in order to find the set of s 0i , s 1i which will give the minimum value of E. However, since this requires voluminous calculations, the shape and the gain are sequentially searched in the present embodiment. Meanwhile, round robin search is used for the combination of s0i and s 1i . There are 32×32 = 1024 combinations for s0i and s 1i . In the following description, s 1i + s 1j are indicated as s m for simplicity. The above equation (27) becomes E = ∥ W '( x - glam)∥ 2 . If, for further simplicity, Xk = W's and s w = W ' s m , we obtain E = Xk - glow 2 E = x w 2 + s 2 g l - x T w · s w s w 2 - ( x T W · s w ) 2 s w 2 Therefore, if gl can be made sufficiently accurate, search can be performed in two steps of (1) searching for s w which will maximize ( x T w · s w ) 2 s w 2 and (1) searching for g 1 which is closest to x T w · s w s w 2 If the above is rewritten using the original notation, (1)' searching is made for a set of s0i and s 1i which will maximize ( x T W ' T W '( s 0i + s 1j )) 2 W '( s 0i + s 1j ) 2 and (2)' searching is made for g 1 which is closest to ( x T W ' T W '( s 0i + s 1j )) W '( s 0i + s 1j ) 2 The above equation (35) represents an optimum encoding condition (nearest neighbor condition). Using the conditions (centroid conditions) of the equations (31) and (32) and the condition of the equation (35), codebooks (CB0, CB1 and CBg) can be trained simultaneously with the use of the so-called generalized Lloyd algorithm (GLA). In the present embodiment, W ' divided by a norm of an input x is used as W '. That is, W '/ ∥ x ∥ is substituted for W ' in the equations (31), (32) and (35). Alternatively, the weighting W ', used for perceptual weighting at the time of vector quantization by the vector quantizer 116, is defined by the above equation (26). However, the weighting W ' taking into account the temporal masking can also be found by finding the current weighting W ' in which past W ' has been taken into account. The values of wh(1), wh(2), ..., wh(L) in the above equation (26), as found at the time n, that is at the n'th frame, are indicated as whn(1), whn(2), ..., whn(L), respectively. If the weights at time n, taking past values into account, are defined as An(i), where 1 ≤ i ≤ L, An(i) = λ A n-1 (i) + (1 - λ) whn(i), (whn(i) ≤ A n-1 (i)) = whn(i), (whn(i) > A n-1 (i)) where λ may be set to, for example, λ = 0.2. In An(i), with 1 ≤ i ≤ L, thus found, a matrix having such An(i) as diagonal elements may be used as the above weighting. The shape index values s 0i , s 1j , obtained by the weighted vector quantization in this manner, are outputted at output terminals 520, 522, respectively, while the gain index gl is outputted at an output terminal 521. Also, the quantized value x 0 ' is outputted at the output terminal 504, while being sent to the adder 505. The adder 505 subtracts the quantized value from the spectral envelope vector x to generate a quantization error vector y . Specifically, this quantization error vector y is sent to the vector quantization unit 511 so as to be dimensionally split and quantized by vector quantizers 511 1 to 511 8 with weighted vector quantization. The second vector quantization unit 510 uses a larger number of bits than the first vector quantization unit 500. Consequently, the memory capacity of the codebook and the processing volume (complexity) for codebook searching are increased significantly. Thus it becomes impossible to carry out vector quantization with the 44-order which is the same as that of the first vector quantization unit 500. Therefore, the vector quantization unit 511 in the second vector quantization unit 510 is made up of plural vector quantizers and the input quantized values are dimensionally split into plural low-dimensional vectors for performing weighted vector quantization. The relation between the quantized values y 0 to y 7 , used in the vector quantizers 511 1 to 511 8 , the number of dimensions and the number of bits are shown in the following Table 2. quantized value dimension number of bits y 0 4 10 y 1 4 10 y 2 4 10 y 3 4 10 y 4 4 9 y 5 8 8 y 6 8 8 y 7 8 7 The index values Id vq0 to Id vq7 outputted from the vector quantizers 511 1 , to 511 8 are outputted at output terminals 523 1 to 523 8 . The sum of bits of these index data is 72. If a value obtained by connecting the output quantized values y 0 ' to y 7 ' of the vector quantizers 511 1 to 511 8 in the dimensional direction is y ', the quantized values y ' and x0' are summed by the adder 513 to give a quantized value x 1 '. Therefore, the quantized value x 1 ' is represented by x 1 '= x 0 ' + y ' = x - y + y ' That is, the ultimate quantization error vector is y ' - y . If the quantized value x 1 ' from the second vector quantizer 510 is to be decoded, the speech signal decoding apparatus is not in need of the quantized value x 1 ' from the first quantization unit 500. However, it is in need of index data from the first quantization unit 500 and the second quantization unit 510. The learning method and code book search in the vector quantization section 511 will be hereinafter explained. As for the learning method, the quantization error vector y is divided into eight low-order vectors y 0 to y 7 , using the weight W ', as shown in Table 2. If the weight W ' is a matrix having 44-point sub-sampled values as diagonal elements: the weight W' is split into the following eight matrices: y and W ', thus split in low dimensions, are termed Y i and WI ', where 1 ≤ i ≤ 8, respectively. The distortion measure E is defined as E = WI ' ( y i - s ) 2 The codebook vector s is the result of quantization of y i . Such code vector of the codebook minimizing the distortion measure E is searched. In the codebook learning, further weighting is done using the general Lloyd algorithm (GLA). The optimum centroid condition for learning is first explained. If there are M input vectors y which have selected the code vector s as optimum quantization result, and the training data is y k , the expected value of distortion J is given by the equation (38) minimizing the center of distortion on weighting with respect to all frames k: Solving, we obtain Taking transposed values of both sides, we obtain Therefore, In the above equation (39), s is an optimum representative vector and represents an optimum centroid condition. As for the optimum encoding condition, it suffices to search for s minimizing the value of ∥ WI ' ( y i - s )∥ 2 . WI 'during searching need not be the same as wi ' during learning and may be non-weighted matrix: By constituting the vector quantization unit 116 in the speech signal encoder by two-stage vector quantization units, it becomes possible to render the number of output index bits variable. The second encoding unit 120 employing the above-mentioned CELP encoder constitution of the present invention, is comprised of multi-stage vector quantization processors as shown in Fig.9. These multi-stage vector quantization processors are formed as two-stage encoding units 120 1 , 120 2 in the embodiment of Fig.9, in which an arrangement for coping with the transmission bit rate of 6 kbps in case the transmission bit rate can be switched between e.g., 2 kbps and 6 kbps, is shown. In addition, the shape and gain index output can be switched between 23 bits/ 5 msec and 15 bits/ 5 msec. The processing flow in the arrangement of Fig.9 is shown in Fig.10. Referring to Fig.9, an LPC analysis circuit 302 of Fig.9 corresponds to the LPC analysis circuit 132 shown in Fig.3, while an LSP parameter quantization circuit 303 corresponds to the constitution from the α to LSP conversion circuit 133 to the LSP to α conversion circuit 137 of Fig.3 and a perceptually weighted filter 304 corresponds to the perceptual weighting filter calculation circuit 139 and the perceptually weighted filter 125 of Fig.3. Therefore, in Fig.9, an output which is the same as that of the LSP to α conversion circuit 137 of the first encoding unit 113 of Fig.3 is supplied to a terminal 305, while an output which is the same as the output of the perceptually weighted filter calculation circuit 139 of Fig.3 is supplied to a terminal 307 and an output which is the same as the output of the perceptually weighted filter 125 of Fig.3 is supplied to a terminal 306. However, in distinction from the perceptually weighted filter 125, the perceptually weighted filter 304 of Fig.9 generates the perceptually weighed signal, that is the same signal as the output of the perceptually weighted filter 125 of Fig.3, using the input speech data and pre-quantization α-parameter, instead of using an output of the LSP-α conversion circuit 137. In the two-stage second encoding units 120 1 and 120 2 , shown in Fig.9, subtractors 313 and 323 correspond to the subtractor 123 of Fig.3, while the distance calculation circuits 314, 324 correspond to the distance calculation circuit 124 of Fig.3. In addition, the gain circuits 311, 321 correspond to the gain circuit 126 of Fig.3, while stochastic codebooks 310, 320 and gain codebooks 315, 325 correspond to the noise codebook 121 of Fig.3. In the constitution of Fig.9, the LPC analysis circuit 302 at step S1 of Fig.10 splits input speech data x supplied from a terminal 301 into frames as described above to perform LPC analysis in order to find an α-parameter. The LSP parameter quantization circuit 303 converts the α-parameter from the LPC analysis circuit 302 into LSP parameters to quantize the LSP parameters. The quantized LSP parameters are interpolated and converted into α-parameters. The LSP parameter quantization circuit 303 generates an LPC synthesis filter function 1/H (z). from the α-parameters converted from the quantized LSP parameters, that is the quantized LSP parameters, and sends the generated LPC synthesis filter function 1/H (z) to a perceptually weighted synthesis filter 312 of the first-stage second encoding unit 120 1 via terminal 305. The perceptual weighting filter 304 finds data for perceptual weighting, which is the same as that produced by the perceptually weighting filter calculation circuit 139 of Fig.3, from the α-parameter from the LPC analysis circuit 302, that is pre-quantization α-parameter. These weighting data are supplied via terminal 307 to the perceptually weighting synthesis filter 312 of the first-stage second encoding unit 120 1 . The perceptual weighting filter 304 generates the perceptually weighted signal, which is the same signal as that outputted by the perceptually weighted filter 125 of Fig.3, from the input speech data and the pre-quantization α-parameter, as shown at step S2 in Fig.10. That is, the LPC synthesis filter function W (z) is first generated from the pre-quantization α-parameter. The filter function W(z) thus generated is applied to the input speech data x to generate Xk which is supplied as the perceptually weighted signal via terminal 306 to the subtractor 303 of the first-stage second encoding unit 120 1 . In the first-stage second encoding unit 120 1 , a representative value output of the stochastic codebook 310 of the 9-bit shape index output is sent to the gain circuit 311 which then multiplies the representative output from the stochastic codebook 310 with the gain (scalar) from the gain codebook 315 of the 6-bit gain index output. The representative value output, multiplied with the gain by the gain circuit 311, is sent to the perceptually weighted synthesis filter 312 with 1/A(z) = (1/H(z))*W(z). The weighting synthesis filter 312 sends the 1/A(z) zero-input response output to the subtractor 313, as indicated at step S3 of Fig.10. The subtractor 313 performs subtraction on the zero-input response output of the perceptually weighting synthesis filter 312 and the perceptually weighted signal Xk from the perceptual weighting filter 304 and the resulting difference or error is taken out as a reference vector r . During searching at the first-stage second encoding unit 120 1 , this reference vector r is sent to the distance calculating circuit 314 where the distance is calculated and the shape vector s and the gain g minimizing the quantization error energy E are searched, as shown at step S4 in Fig.10. Here, 1/A(z) is in the zero state. That is, if the shape vector s in the codebook synthesized with 1/A(z) in the zero state is s syn , the shape vector s and the gain g minimizing the equation (40): are searched. Although s and g minimizing the quantization error energy E may be full-searched, the following method may be used for reducing the amount of calculations. The first method is to search the shape vector s minimizing E s defined by the following equation (41): From s obtained by the first method, the ideal gain is as shown by the equation (42): Therefore, as the second method, such g minimizing the equation (43): Eg = (g ref - g) 2 is searched. Since E is a quadratic function of g, such g minimizing Eg minimizes E. From s and g obtained by the first and second methods, the quantization error vector e can be calculated by the following equation (44): e = r - g s syn This is quantized as a reference of the second-stage second encoding unit 120 2 as in the first stage. That is, the signal supplied to the terminals 305 and 307 are directly supplied from the perceptually weighted synthesis filter 312 of the first-stage second encoding unit 120 1 to a perceptually weighted synthesis filter 322 of the second stage second encoding unit 120 2 . The quantization error vector e found by the first-stage second encoding unit 120 1 is supplied to a subtractor 323 of the second-stage second encoding unit 120 2 . At step S5 of Fig.10, processing similar to that performed in the first stage occurs in the second-stage second encoding unit 120 2 . That is, a representative value output from the stochastic codebook 320 of the 5-bit shape index output is sent to the gain circuit 321 where the representative value output of the codebook 320 is multiplied with the gain from the gain codebook 325 of the 3-bit gain index output. An output of the weighted synthesis filter 322 is sent to the subtractor 323 where a difference between the output of the perceptually weighted synthesis filter 322 and the first-stage quantization error vector e is found. This difference is sent to a distance calculation circuit 324 for distance calculation in order to search the shape vector s and the gain g minimizing the quantization error energy E. The shape index output of the stochastic codebook 310 and the gain index output of the gain codebook 315 of the first-stage second encoding unit 120 1 and the index output of the stochastic codebook 320 and the index output of the gain codebook 325 of the second-stage second encoding unit 120 2 are sent to an index output switching circuit 330. If 23 bits are outputted from the second encoding unit 120, the index data of the stochastic codebooks 310, 320 and the gain codebooks 315, 325 of the first-stage and second-stage second encoding units 120 1 , 120 2 are summed and outputted. If 15 bits are outputted, the index data of the stochastic codebook 310 and the gain codebook 315 of the first-stage second encoding unit 120 1 are outputted. The filter state is then updated for calculating zero-input response output as shown at step S6. In the present embodiment, the number of index bits of the second-stage second encoding unit 120 2 is as small as 5 for the shape vector, while that for the gain is as small as 3. If suitable shape and gain are not present in this case in the codebook, the quantization error is likely to be increased, instead of being decreased. Although 0 may be provided in the gain for preventing such defect, there are only three bits for the gain. if one of these is set to 0, the quantizer performance is significantly deteriorated. In this consideration, all-0 vector is provided for the shape vector to which a larger number of bits have been allocated. The above-mentioned search is performed, with the exclusion of the all-zero vector, and the all-zero vector is selected if the quantization error has ultimately been increased. The gain is arbitrary. This makes it possible to prevent the quantization error from being increased in the second-stage second encoding unit 120 2 . Although the two-stage arrangement has been described above, the number of stages may be larger than 2. In such case, if the vector quantization by the first-stage closed-loop search has come to a close, quantization of the N'th stage, where 2 ≤ N, is carried out with the quantization error of the (N-1)st stage as a reference input, and the quantization error of the of the N'th stage is used as a reference input to the (N+1)st stage. It is seen from Figs.9 and 10 that, by employing multi-stage vector quantizers for the second encoding unit, the amount of calculations is decreased as compared to that with the use of a straight vector quantization with the same number of bits or with the use of a conjugate codebook. In particular, in CELP encoding in which vector quantization of the time-axis waveform employing the closed-loop search by the analysis by synthesis method, a smaller number of times of search operations is crucial. In addition, the number of bits can be easily switched by switching between employing both index outputs of the two-stage second encoding units 120 1 , 120 2 and employing only the output of the first-stage second encoding unit 120 1 without employing the output of the second-stage second encoding unit 120 1 . If the index outputs of the first-stage and second-stage second encoding units 120 1 , 120 2 are combined and outputted, the decoder can easily cope with the configuration by selecting one of the index outputs. That is, That is, the decoder can easily cope with the configuration by decoding the parameter encoded with e.g., 6 kbps using a decoder operating at 2 kbps. In addition, if zero-vector is contained in the shape codebook of the second-stage second encoding unit 120 2 , it becomes possible to prevent the quantization error from being increased with lesser deterioration in performance than if 0 is added to the gain. The code vector of the stochastic codebook, for example, can be generated by clipping the so-called Gaussian noise. Specifically, the codebook may be generated by generating the Gaussian noise, clipping the Gaussian noise with a suitable threshold value and normalizing the clipped Gaussian noise. However, there are a variety of types in the speech. For example, the Gaussian noise can cope with speech of consonant sounds close to noise, such as "sa, shi, su, se and so", while the Gaussian noise cannot cope with the speech of acutely rising consonants, such as "pa, pi, pu, pe and po". According to the present invention, the Gaussian noise is applied to some of the code vectors, while the remaining portion of the code vectors is dealt with by learning, so that both the consonants having sharply rising consonant sounds and the consonant sounds close to the noise can be coped with. If, for example, the threshold value is increased, such vector is obtained which has several larger peaks, whereas, if the threshold value is decreased, the code vector is approximate to the Gaussian noise. Thus, by increasing the variation in the clipping threshold value, it becomes possible to cope with consonants having sharp rising portions, such as "pa, pi, pu, pe and po" or consonants close to noise, such as "sa, shi, su, se and so", thereby increasing clarity. Fig.11 shows the appearance of the Gaussian noise and the clipped noise by a solid line and by a broken line, respectively. Figs.11A and 11B show the noise with the clipping threshold value equal to 1.0, that is with a larger threshold value, and the noise with the clipping threshold value equal to 0.4, that is with a smaller threshold value. It is seen from Figs.11A and 11B that, if the threshold value is selected to be larger, there is obtained a vector having several larger peaks, whereas, if the threshold value is selected to a smaller value, the noise approaches to the Gaussian noise itself. For realizing this, an initial codebook is prepared by clipping the Gaussian noise and a suitable number of non-learning code vectors are set. The non-learning code vectors are selected in the order of the increasing variance value for coping with consonants close to the noise, such as "sa, shi, su, se and so". The vectors found by learning use the LBG algorithm for learning. The encoding under the nearest neighbor condition uses both the fixed code vector and the code vector obtained on learning. In the centroid condition, only the code vector set for learning is updated. Thus the code vector set for learning can cope with sharply rising consonants, such as "pa, pi, pu, pe and po". An optimum gain may be learned for these code vectors by usual learning. Fig.12 shows the processing flow for the constitution of the codebook by clipping the Gaussian noise. In Fig.12, the number of times of learning n is set to n = 0 at step S10 for initialization. With an error D 0 = ∞, the maximum number of times of learning n max is set and a threshold value ∈ setting the learning end condition is set. At the next step S11, the initial codebook by clipping the Gaussian noise is generated. At step S12, part of the code vectors is fixed as non-learning code vectors. At the next step S13, encoding is done sing the above codebook. At step S14, the error is calculated. At step S15, it is judged if (D n-1 - D n / D n < ∈, or n = n max If the result is YES, processing is terminated. If the result is NO, processing transfers to step S16. At step S16, the code vectors not used for encoding are processed. At the next step S17, the code books are updated. At step S18, the number of times of learning n is incremented before returning to step S13. The above-described signal encoding and signal decoding apparatus may be used as a speech codebook employed in, for example, a portable communication terminal or a portable telephone set shown in fig.14. Fig.13 shows a transmitting side of a portable terminal employing a speech encoding unit 160 configured as shown in Figs.1 and 3. The speech signals collected by a microphone 161 are amplified by an amplifier 162 and converted by an analog/digital (A/D) converter 163 into digital signals which are sent to the speech encoding unit 160 configured as shown in Figs.1 and 3. The digital signals from the A/D converter 163 are supplied to the input terminal 101. The speech encoding unit 160 performs encoding as explained in connection with Figs.1 and 3. Output signals of output terminals of Figs. 1 and 2 are sent as output signals of the speech encoding unit 160 to a transmission channel encoding unit 164 which then performs channel coding on the supplied signals. Output signals of the transmission channel encoding unit 164 are sent to a modulation circuit 165 for modulation and thence supplied to an antenna 168 via a digital/analog (D/A) converter 166 and an RF amplifier 167. Fig.14 shows a reception side of a portable terminal employing a speech decoding unit 260 configured as shown in Fig.4. The speech signals received by the antenna 261 of Fig.14 are amplified an RF amplifier 262 and sent via an analog/digital (AID) converter 263 to a demodulation circuit 264, from which demodulated signal are sent to a transmission channel decoding unit 265. An output signal of the decoding unit 265 is supplied to a speech decoding unit 260 configured as shown in Figs.2 and 4. The speech decoding unit 260 decodes the signals as explained in connection with Figs.2 and 4. An output signal at an output terminal 201 of Figs.2 and 4 is sent as a signal of the speech decoding unit 260 to a digital/analog (D/A) converter 266. An analog speech signals from the D/A converter 266 is sent to a speaker 268. The present invention as defined by the appended claims is not limited to the above-described embodiments. For example, the configuration of the speech synthesis side (encoder) or the speech synthesis side (decoder), so far described as a hardware, can also be realized by a software program using a so-called digital signal processor (DSP). Also, data of plural frames may be collected together and quantized by matrix quantization instead of by vector quantization. Moreover, the speech encoding method or a corresponding speech decoding method may also be applied not only to the speech synthesis/analysis method employing multi-band excitation described above but also to a variety of speech synthesis/analysis methods such as those synthesizing voided portions of the speech by sinusoidal synthesis and synthesizing the unvoiced speech portions based on noise signals. The application may also be applied to wise fields of application. That is, the present invention as defined by the appended claims is not limited to transmission or recording/reproduction but also may be applied to pitch conversion, speed medication or noise suppression.

Claims: A speech encoding method for dividing an input speech signal on the time axis into blocks as units and encoding the resulting signal, comprising: an encoding step of vector-quantizing by a time-domain closed loop search of an optimum vector using an analysis by synthesis wherein a codebook generated by clipping the Gaussian noise at a plurality of threshold values is used as a codebook for said vector quantization. The speech signal encoding method as claimed in claim 1 wherein the codebook for said vector quantization is made up of a code vector generated on clipping said Gaussian noise and a codebook vector obtained by learning using the codebook vector generated by clipping the Gaussian noise as initial value. A speech encoding apparatus for dividing an input speech signal on the time axis into blocks as units and encoding the resulting signal, comprising: encoding means for vector-quantizing by a time-domain closed loop search of an optimum vector using an analysis by synthesis method wherein a codebook generated by clipping the Gaussian noise at a plurality of threshold values is used as a codebook for said vector quantization. The speech signal encoding apparatus as claimed in claim 3 wherein the codebook for vector quantization is made up of a code vector generated on clipping said Gaussian noise and a codebook vector obtained by learning using the codebook vector generated by clipping the Gaussian noise as initial value. A portable radio terminal apparatus comprising: amplifier means for amplifying an input speech signal; A/D conversion means for A/D converting the resulting amplified signal; speech encoding means for speech-encoding an output of said A/D converting means; transmission path encoding means for channel-decoding said encoded signal; and modulation means for amplifying a signal from said D/A converting means and for supplying the amplified signal to an antenna; said speech encoding means further comprising encoding means for vector-quantizing by a time-domain closed loop search of an optimum vector using an analysis by synthesis method wherein a codebook generated by clipping the Gaussian noise at a plurality of threshold values is used as a codebook for said vector quantization.

Inventors: IIJIMA KAZUYUKI, MATSUMOTO JUN, NISHIGUCHI MASAYUKI, OMORI SHIRO, IIJIMA, KAZUYUKI, MATSUMOTO, JUN, NISHIGUCHI, MASAYUKI, OMORI, SHIRO
Applicants: SONY CORP, SONY CORPORATION
Application year: 1996
Main classification: G10L19
IPC codes: H03M7, G10L19