Method for encoding speech

Abstract: The reduncancy contained within the spectral amplitudes is reduced, and as a result the quantization of the spectral amplitudes is improved. The prediction of the spectral amplitudes of the current segment from the spectral amplitudes of the previous is adjusted to account for any change in the fundamental frequency between the two segments. The spectral amplitudes prediction residuals are divided into a fixed number of blocks each containing approximately the same number of elements. A prediction residual block average (PRBA) vector is formed; each element of the PRBA is equal to the average of the prediction residuals within one of the blocks. The PRBA vector is vector quantized, or it is transformed with a Discrete Cosine Transform (DCT) and scalar quantized. The perceived effect of bit errors is reduced by smoothing the voiced/unvoiced decisions. An estimate of the error rate is made by locally averaging the number of correctable bit errors within each segment. If the estimate of the error rate is greater than a threshold, then high energy spectral amplitudes are declared voiced.

Description: This invention relates to methods for encoding speech, for enhancing speech and for synthesizing speech and provides examples of methods which can preserve the quality of speech during the presence of bit errors in a speech signal. Relevant publications include: J. L. Flanagan. Speech Analysis, Synthesis and Perception, Springer-Verlag, 1972, pp. 378-386, (discusses phase vocoder - frequency-based speech analysis-synthesis system); Quatieri, et al., "Speech Transformations Based on a Sinusoidal Representation", IEEE TASSP, Vol. ASSP34. No. 6, Dec. 1986, pp. 1449-1986, (discusses analysis-synthesis technique based on a sinusoidal representation); Griffin, "Multiband Excitation Vocoder", Ph.D. Thesis, M.I.T, 1987, (discusses an 8000 bps Multi-Band Excitation speech coder): Griffin, et al., "A High Quality 9.6 kbps Speech Coding System", Proc. ICASSP 86, pp. 125-128, Tokyo, Japan, April 13-20, 1986, (discusses a 9600 bps Multi-Band Excitation speech coder); Griffin, et al., "A New Model-Based Speech Analysis/Synthesis System", Proc. ICASSP 85, pp. 513-516. Tampa. FL., March 26-29, 1985, (discusses Multi-Band Excitation speech model); Hardwick, "A 4.8 kbps Multi-Band Excitation Speech Coder", S.M. Thesis, M.I.T, May 1988, (discusses a 4800 bps Multi-Band Excitation speech coder); McAulay et al., "Mid-Rate Coding Based on a Sinusoidal Representation of Speech". Proc. ICASSP 85, pp. 945-948. Tampa, FL., March 26-29, 1985. (discusses speech coding based on a sinusoidal representation); Campbell et al., "The New 4800 bps Voice Coding Standard", Mil Speech Tech Conference, Nov. 1989, (discusses error correction in low rate speech coders); Campbell et al., "CELP Coding for Land Mobile Radio Applications", Proc. ICASSP 90, pp. 465-468, Albequerque, NM. April 3-6, 1990, (discusses error correction in low rate speech coders); Levesque et al., Error-Control Techniques for Digital Communication , Wiley, 1985, pp. 157-170, (discusses error correction in general); Jayant et al., Digital Coding of Waveforms, Prentice-Hall, 1984 (discusses quantization in general); Makhoul, et.al. "Vector Quantization in Speech Coding", Proc. IEEE, 1985, pp. 1551-1588 (discusses vector quantization in general); Jayant et al., "Adaptive Postfiltering of 16 kb/s-ADPCM Speech", Proc. ICASSP 86, pp. 829-832. Tokyo, Japan, April 13-20, 1986, (discusses adaptive postfiltering of speech). The contents of these publications are incorporated herein by reference. The problem of speech coding (compressing speech into a small number of bits) has a large number of applications, and as a result has received considerable attention in the literature. One class of speech coders (vocoders) which have been extensively studied and used in practice is based on an underlying model of speech. Examples from this class of vocoders include linear prediction vocoders, homomorphic vocoders, and channel vocoders. In these vocoders, speech is modeled on a short-time basis as the response of a linear system excited by a periodic impulse train for voiced sounds or random noise for unvoiced sounds. For this class of vocoders, speech is analyzed by first segmenting speech using a window such as a Hamming window. Then, for each segment of speech, the excitation parameters and system parameters are estimated and quantized. The excitation parameters consist of the voiced/unvoiced decision and the pitch period. The system parameters consist of the spectral envelope or the impulse response of the system. In order to reconstruct speech, the quantized excitation parameters are used to synthesize an excitation signal consisting of a periodic impulse train in voiced regions or random noise in unvoiced regions. This excitation signal is then filtered using the quantized system parameters. Even though vocoders based on this underlying speech model have been quite successful in producing intelligible speech, they have not been successful in producing high-quality speech. As a consequence, they have not been widely used for high-quality speech coding. The poor quality of the reconstructed speech is in part due to the inaccurate estimation of the model parameters and in part due to limitations in the speech model. A new speech model, referred to as the Multi-Band Excitation (MBE) speech model, was developed by Griffin and Lim in 1984. Speech coders based on this new speech model were developed by Griffin and Lim in 1986, and they were shown to be capable of producing high quality speech at rates above 8000 bps (bits per second). Subsequent work by Hardwick and Lim produced a 4800 bps MBE speech coder which was also capable of producing high quality speech. This 4800 bps speech coder used more sophisticated quantization techniques to achieve similar quality at 4800 bps that earlier MBE speech coders had achieved at 8000 bps. The 4800 bps MBE speech coder used a MBE analysis/synthesis system to estimate the MBE speech model parameters and to synthesize speech from the estimated MBE speech model parameters. A discrete speech signal, denoted by s ( n ), is obtained by sampling an analog speech signal. This is typically done at an 8 kHz, sampling rate, although other sampling rates can easily be accommodated through a straightforward change in the various system parameters. The system divides the discrete speech signal into small overlapping segments or segments by multiplying s ( n ) with a window w ( n ) (such as a Hamming window or a Kaiser window) to obtain a windowed signal s w ( n ). Each speech segment is then analyzed to obtain a set of MBE speech model parameters which characterize that segment. The MBE speech model parameters consist of a fundamental frequency, which is equivalent to the pitch period, a set of voiced/unvoiced decisions, a set of spectral amplitudes, and optionally a set of spectral phases. These model parameters are then quantized using a fixed number of bits for each segment. The resulting bits can then be used to reconstruct the speech signal, by first reconstructing the MBE model parameters from the bits and then synthesizing the speech from the model parameters. A block diagram of a typical MBE speech coder is shown in Figure 1. The 4800 bps MBE speech coder required the use of a sophisticated technique to quantize the spectral amplitudes. For each speech segment the number of bits which could be used to quantize the spectral amplitudes varied between 50 and 125 bits. In addition the number of spectral amplitudes for each segment varies between 9 and 60. A quantization method was devised which could efficiently represent all of the spectral amplitudes with the number of bits available for each segment. Although this spectral amplitude quantization method was designed for use in an MBE speech coder the quantization techniques are equally useful in a number of different speech coding methods, such as the Sinusoidal Transform Coder and the Harmonic Coder . For a particular speech segment, L and denotes the number of spectral amplitudes in that segment. The value of L and is derived from the fundamental frequency, ω and 0 , according to the relationship, L = └ β └ π ω 0 + .25┘┘ where 0 ≤ β ≤ 1.0 determines the speech bandwidth relative to half the sampling rate. The function └ x ┘, referred to in Equation (1), is equal to the largest integer less than or equal to x . The L and spectral amplitudes are denoted by M and l for 1 ≤ l ≤ L and , where M and l is the lowest frequency spectral amplitude and M and L is the highest frequency spectral amplitude. The spectral amplitudes for the current speech segment are quantized by first calculating a set of prediction residuals which indicate the amount the spectral amplitudes have changed between the current speech segment and the previous speech segment. If L and 0 denotes the number of spectral amplitudes in the current speech segment and L and -1 denotes the number of spectral amplitudes in the previous speech segment, then the prediction residuals, T and l for 1 ≤ l ≤ L and 0 are given by, where M and 0 / l denotes the spectral amplitudes of the current speech segment and M and -1 / l denotes the quantized spectral amplitudes of the previous speech segment. The constant γ is typically equal to .7, however any value in the range 0 ≤ γ ≤ 1 can be used. The prediction residuals are divided into blocks of K elements, where the value of K is typically in the range 4 ≤ K ≤ 12. If L and is not evenly divisible by K , then the highest frequency block will contain less than K elements. This is shown in Figure 2 for L and = 34 and K = 8. Each of the prediction residual blocks is then transformed using a Discrete Cosine Transform (DCT) defined by, The length of the transform for each block, J , is equal to the number of elements in the block. Therefore, all but the highest frequency block are transformed with a DCT of length K , while the length of the DCT for the highest frequency block is less than or equal to K . Since the DCT is an invertible transform, the L and DCT coefficients completely specify the spectral amplitude prediction residuals for the current segment. The total number of bits available for quantizing the spectral amplitudes is divided among the DCT coefficients according to a bit allocation rule. This rule attempts to give more bits to the perceptually more important low-frequency blocks, than to the perceptually less important high-frequency blocks. In addition the bit allocation rule divides the bits within a block to the DCT coefficients according to their relative long-term variances. This approach matches the bit allocation with the perceptual characteristics of speech and with the quantization properties of the DCT. Each DCT coefficient is quantized using the number of bits specified by the bit allocation rule. Typically, uniform quantization is used, however non-uniform or vector quantization can also be used. The step size for each quantizer is determined from the long-term variance of the DCT coefficients and from the number of bits used to quantize each coefficient. Table 1 shows the typical variation in the step size as a function of the number of bits, for a long-term variance equal to σ 2 . Once each DCT coefficient has been quantized using the number of bits specified by the bit allocation rule, the binary representation can be transmitted, stored, etc.. Step Size of Uniform Quantizers Number of Bits Step Size 1 1.2σ 2 .85σ 3 .65σ 4 .42σ 5 .28σ 6 .14σ 7 .07σ 8 .035σ 9 .0175σ 10 .00875σ 11 .00438σ 12 .00219σ 13 .00110σ 14 .000550σ 15 .000275σ 16 .000138σ depending on the application. The spectral amplitudes can be reconstructed from the binary representation by first reconstructing the quantized DCT coefficients for each block, performing the inverse DCT on each block, and then combining with the quantized spectral amplitudes of the previous segment using the inverse of Equation (2). The inverse DCT is given by, where the length, J , for each block is chosen to be the number of elements in that block, α( j ) is given by, One potential problem with the 4800 bps MBE speech coder is that the perceived quality of the reconstructed speech may be significantly reduced if bit errors are added to the binary representation of the MBE model parameters. Since bit errors exist in many speech coder applications, a robust speech coder must be able to correct, detect and/or tolerate bit errors. One technique which has been found to be very successful is to use error correction codes in the binary representation of the model parameters. Error correction codes allow infrequent bit errors to be corrected, and they allow the system to estimate the error rate. The estimate of the error rate can then be used to adaptively process the model parameters to reduce the effect of any remaining bit errors. Typically, the error rate is estimated by counting the number of errors corrected (or detected) by the error correction codes in the current segment, and then using this information to update the current estimate of error rate. For example if each segment contains a (23,12) Golay code which can correct three errors out of the 23 bits, and ε T denotes the number of errors (0-3) which were corrected in the current segment, then the current estimate of the error rate, ε R , is updated according to: ε R = 3 ε R + (1 - 3 ) ε T 23 where β is a constant in the range 0 ≤ β ≤ 1 which controls the adaptability of ε R . When error correction codes or error detection codes are used, the bits representing the speech model parameters are converted to another set of bits which are more robust to bit errors. The use of error correction or detection codes typically increases the number of bits which must be transmitted or stored. The number of extra bits which must be transmitted is usually related to the robustness of the error correction or detection code. In most applications, it is desirable to minimize the total number of bits which are transmitted or stored. In this case the error correction or detection codes must be selected to maximize the overall system performance. Another problem in this class of speech coding systems is that limitations in the estimation of the speech model parameters may cause quality degradation in the synthesized speech. Subsequent quantization of the model parameters induces further degradation. This degradation can take the form of reverberant or muffled quality to the synthesized speech. In addition background noise or other artifacts may be present which did not exist in the orignal speech. This form of degradation occurs even if no bit errors are present in the speech data, however bit errors can make this problem worse. Typically speech coding systems attempt to optimize the parameter estimators and parameter quantizers to minimize this form of degradation. Other systems attempt to reduce the degradations by post-filtering. In post-filtering the output speech is filtered in the time domain with an adaptive all-pole filter to sharpen the format peaks. This method does not allow fine control over the spectral enhancement process and it is computationally expensive and inefficient for frequency domain speech coders. The invention described herein applies to many different speech coding methods, which include but are not limited to linear predictive speech coders, channel vocoders, homomorphic vocoders, sinusoidal transform coders, multi-band excitation speech coders and improved multiband excitation (IMBE) speech coders. For the purpose of describing this invention in detail, we use the 6.4 kbps IMBE speech coder which has recently been standardized as part of the INMARSAT-M (International Marine Satellite Organization) satellite communication system. This coder uses a robust speech model which is referred to as the Multi-Band Excitation (MBE) speech model. Efficient methods for quantizing the MBE model parameters have been developed. These methods are capable of quantizing the model parameters at virtually any bit rate above 2 kbps. The 6.4 kbps IMBE speech coder used in the INMARSAT-M satellite communication system uses a 50 Hz frame rate. Therefore 128 bits are available per frame. Of these 128 bits, 45 bits are reserved for forward error correction. The remaining 83 bits per frame are used to quantize the MBE model parameters, which consist of a fundamental frequency ω and 0 , a set of V/UV decisions υ and k for 1 ≤ k < K and , and a set of spectral amplitudes M and l for 1 ≤ l ≤ L. The values of K and and L and vary depending on the fundamental frequency of each frame. The 83 available bits are Bit Allocation Among Model Parameters Parameter Number of Bits Fundamental Frequency S Voiced/Unvoiced Decisions K and Spectral Amplitudes 75 ― K and divided among the model parameters as shown in Table 2. The fundamental frequency is quantized by first converting it to its equivalent pitch period using Equation (7). P 0 = 2π ω 0 The value of P and 0 is typically restricted to the range 20 ≤ P and 0 ≤ 120 assuming an 8 kHz sampling rate. In the 6.4 kbps IMBE system this parameter is uniformly quantized using 8 bits and a step size of .5. This corresponds to a pitch accuracy of one half sample. The K and V/UV decisions are binary values. Therefore they can be encoded using a single bit per decision. The 6.4 kbps system uses a maximum of 12 decisions, and the width of each frequency band is equal to 3ω and 0 . The width of the highest frequency band is adjusted to include frequencies up to 3.8 kHz. The spectral amplitudes are quantized by forming a set of prediction residuals. Each prediction residual is the difference between the logarithm of the spectral amplitude for the current frame and the logarithm of the spectral amplitude representing the same frequency in the previous speech frame. The spectral amplitude prediction residuals are then divided into six blocks each containing approximately the same number of prediction residuals. Each of the six blocks is then transformed with a Discrete Cosine Transform (DCT) and the D.C. coefficients from each of the six blocks are combined into a 6 element Prediction Residual Block Average (PRBA) vector. The mean is subtracted from the PRBA vector and quantized using a 6 bit non-uniform quantizer. The zero-means PRBA vector is then vector quantized using a 10 bit vector quantizer. The 10 bit PRBA code-book was designed using a k-means clustering algorithm on a large training set consisting of zero-means PRBA vectors from a variety of speech material. The higher-order DCT coefficients which are not included in the PRBA vector are quantized with scalar uniform quantizers using the 59 ― K remaining bits. The bit allocation and quantizer step sizes are based upon the long term variances of the higher order DCT coefficients. There are several advantages to this quantization method. First, it provides very good fidelity using a small number of bits and it maintains this fidelity as L varies over its range. In addition the computational requirements of this approach are well within the limits required for real-time implementation using a single DSP such as the AT&T DSP32C. Finally this quantization method separates the spectral amplitudes into a few components such as the mean of the PRBA vector, which are sensitive to bit errors, and a large number of other components which are not very sensitive to bit errors. Forward error correction can then be used in an efficient manner by providing a high degree of protection for the few sensitive components and a lesser degree of protection for the remaining components. This is discussed in the next section. According to the present invention, there is provided a method of encoding speech wherein the speech is broken into segments, each of said segments representing one of a succession of time intervals and having a spectrum of frequencies, and for each segment the spectrum is sampled at a set of frequencies to form a set of actual spectral amplitudes, with the frequencies at which the spectrum is sampled generally differing from one segment to the next, and wherein the spectral amplitudes for at least one previous segment are used to produce a set of predicted spectral amplitudes for a current segment, and wherein a set of prediction residuals for the current segment based on a difference between the actual spectral amplitudes for the current segment and the predicted spectral amplitudes for the current segment are used in subsequence encoding, is characterized in that the predicted spectral amplitudes for the current segment are based at least in part on interpolating the spectral amplitudes of a previous segment to estimate the spectral amplitudes in the previous segment at the frequencies of the current segment. Other features and advantages of the invention will be apparent from the following description of preferred embodiments. In the drawings: Figures 1 to 2 are diagrams showing prior art speech coding methods. Figure 3 is a flow chart showing a preferred embodiment of the invention in which the spectral amplitude prediction accounts for any change in the fundamental frequency. Figure 4 is a flow chart showing a preferred embodiment of the invention in which the spectral amplitudes are divided into a fixed number of blocks. Figure 5 is a flow chart showing a preferred embodiment of the invention in which a prediction residual block average vector is formed. Figure 6 is a flow chart showing a preferred embodiment of the invention in which the prediction residual block average vector is vector quantized. Figure 7 is a flow chart showing a preferred embodiment of the invention in which the prediction residual block average vector is quantized with a DCT and scalar quantization. Figure 8 is a flow chart showing a preferred embodiment of the invention encoder in which different error correction codes are used for different model parameter bits. Figure 9 is a flow chart showing a preferred embodiment of the invention decoder in which different error correction codes are used for different model parameter bits. Figure 10 is a flow chart showing a preferred embodiment of the invention in which frequency domain spectral envelope parameter enhancement is depicted. In the prior art, the spectral amplitude prediction residuals were formed using Equation (2). This method does not account for any change in the fundamental frequency between the previous segment and current segment. In order to account for the change in the fundamental frequency a new method has been developed which first interpolates the spectral amplitudes of the previous segment. This is typically done using linear interpolation, however various other forms of interpolation could also be used. Then the interpolated spectral amplitudes of the previous segment are resampled at the frequency points corresponding to the multiples of the fundamental frequency of the current segment. This combination of interpolation and resampling produces a set of predicted spectral amplitudes, which have been corrected for any inter-segment change in the fundamental frequency. Typically a fraction of the base two logarithm of the predicted spectral amplitudes is subtracted from the base two logarithm of the spectral amplitudes of the current segment. If linear interpolation is used to compute the predicted spectral amplitudes. then this can be expressed mathematically as: T l = log 2 M 0 l - γ[(1 - δ l ) log 2 M -1 k l + δ l log 2 M -1 kl +1 ] where δ l is given by, δ l = ω 0 0 ω -1 0 · l - └ ω 0 0 ω -1 0 · l ┘ where γ is a constant subject to 0 ≤ γ ≤ 1. Typically, γ = .7, however other values of γ can also be used. For example γ could be adaptively changed from segment to segment in order to improve performance. The parameters ω and 0 / 0 and ω and -1 / 0 in Equation (9) refer to the fundamental frequency of the current segment and the previous segment, respectively. In the case where the two fundamental frequencies are the same, the new method is identical to the old method. In other cases the new method produces a prediction residual with lower variance than the old method. This allows the prediction residuals to be quantized with less distortion for a given number of bits. In another aspect of the invention a new method has been developed to divide the spectral amplitude prediction residuals into blocks. In the old method the L and prediction residuals from the current segment were divided into blocks of K elements, where K = 8 is a typical value. Using this method, the characteristics of each block were found to be significantly different for large and small values of L and . This reduced the quantization efficiency, thereby increasing the distortion in the spectral amplitudes. In order to make the characteristics of each block more uniform, a new method was divised which divides the L and prediction residuals into a fixed number of blocks. The length of each block is chosen such that all blocks within a segment have nearly the same length, and the sum of the lengths of all the blocks within a segment equal L and . Typically the total number of prediction residuals is divided into 6 blocks, where the length of each block is equal to └ L and / 6┘. If L and is not evenly divisible by 6 then the length of one or more higher frequency blocks is increased by one, such that all of the spectral magnitudes are included in one of the six blocks. This new method is shown in Figure 4 for the case where 6 blocks are used and L and = 34. In this new method the approximate percentage of the prediction residuals contained in each block is independent of L and . This reduces the variation in the characteristics of each block, and it allows more efficient quantization of the prediction residuals. The quantization of the prediction residuals can be further improved by forming a prediction residual block average (PRBA) vector. The length of the PRBA vector is equal to the number of blocks in the current segment. The elements of this vector correspond to the average of the prediction residuals within each block. Since the first DCT coefficient is equal to the average (or D.C. value), the PRBA vector can be formed from the first DCT coefficient from each block. This is shown in Figure 5 for the case where 6 blocks are present in the current segment and L and = 34. This process can be generalized by forming additional vectors from the second (or third, fourth, etc.) DCT coefficient from each block. The elements of the PRBA vector are highly correlated. Therefore a number of methods can be used to improve the quantization of the spectral amplitudes. One method which can be used to achieve very low distortion with a small number of bits is vector quantization. In this method a codebook is designed which contains a number of typical PRBA vectors. The PRBA vector for the current segment is compared against each of the codebook vectors, and the one with the lowest error is chosen as the quantized PRBA vector. The codebook index of the chosen vector is used to form the binary representation of the PRBA vector. A method for performing vector quantization of the PRBA vector has been developed which uses the cascade of a 6 bit non-uniform quantizer for the mean of the vector, and a 10 bit vector quantizer for the remaining information. This method is shown in Figure 6 for the case where the PRBA vector always contains 6 elements. Typical values for the 6 bit and 10 bit quantizers are given in the attached appendix. An alternative method for quantizing the PRBA vector has also been developed. This method requires less computation and storage than the vector quantization method. In this method the PRBA vector is first transformed with a DCT as defined in Equation (3). The length of the DCT is equal to the number of elements in the PRBA vector. The DCT coefficients are then quantized in a manner similar to that discussed in the prior art. First a bit allocation rule is used to distribute the total number of bits used to quantize the PRBA vector among the DCT coefficients. Scalar quantization (either uniform or non-uniform) is then used to quantize each DCT coefficient using the number of bits specified by the bit allocation rule. This is shown in Figure 7 for the case where the PRBA vector always contains 6 elements. Various other methods can be used to efficiently quantize the PRBA vector. For example other transforms such as the Discrete Fourier Transform, the Fast Fourier Transform, the Karhunen-Louve Transform could be used instead of the DCT. In addition vector quantization can be combined with the DCT or other transform. The improvements derived from this aspect of the invention can be used with a wide variety of quantization methods. In another aspect a new method for reducing the perceptual effect of bit errors has been developed. Error correction codes are used as in the prior art to correct infrequent bit errors and to provide an estimate of the error rate ε R . The new method uses the estimate of the error rate to smooth the voiced/unvoiced decisions, in order to reduce the perceived effect of any remaining bit errors. This is done by first comparing the error rate against a threshold which signifies the rate at which the distortion from uncorrected bit errors in the voiced/unvoiced decisions is significant. The exact value of this threshold depends on the amount of error correction applied to the voiced/unvoiced decisions, but a threshold value of .003 is typical if little error correction has been applied. If the estimated error rate, ε R , is below this threshold then the voiced/unvoiced decisions are not perturbed. If ε R is above this threshold then every spectral amplitude for which Equation (10) is satisfied is declared voiced. Although Equation (10) assumes a threshold value of .003, this method can easily be modified to accommodate other thresholds. The parameter S E is a measure of the local average energy contained in the spectral amplitudes. This parameter is typically updated each segment according to: where R 0 is given by, The initial value of S E is set to an arbitrary initial value in the range 0 ≤ S E ≤ 10000.0. The purpose of this parameter is to reduce the dependency of Equation (10) on the average signal level. This ensures that the new method works as well for low level signals as it does for high level signals. The specific forms of Equations (10), (11) and (12) and the constants contained within them can easily be modified, while maintaining the essential components of the new method. The main components of this new method are to first use an estimate of the error rate to determine whether the voiced/unvoiced decisions need to be smoothed. Then if smoothing is required, the voiced/unvoiced decisions are perturbed such that all high energy spectral amplitudes are declared voiced. This eliminates any high energy voiced to unvoiced or unvoiced to voiced transitions between segments, and as a result it improves the perceived quality of the reconstructed speech in the presence of bit errors. In our invention we divide the quantized speech model parameter bits into three or more different groups according to their sensitivity to bit errors, and then we use different error correction or detection codes for each group. Typically the group of data bits which is determined to be most sensitive to bit errors is protected using very effective error correction codes. Less effective error correction or detection codes, which require fewer additional bits, are used to protect the less sensitive data bits. This new method allows the amount of error correction or detection given to each group to be matched to its sensitivity to bit errors. Compared to the prior art, this method has the advantage that the degradation caused by bit errors is reduced and the number of bits required for forward error correction is also reduced. The particular choice of error correction or detection codes which is used depends upon the bit error statistics of the transmission or storage medium and the desired bit rate. The most sensitive group of bits is typically protected with an effective error correction code such as a Hamming code, a BCH code, a Golay code or a Reed-Solomon code. Less sensitive groups of data bits may use these codes or an error detection code. Finally the least sensitive groups may use error correction or detection codes or they may not use any form of error correction or detection. The invention is described herein using a particular choice of error correction and detection codes which was well suited to a 6.4 kbps IMBE speech coder for satellite communications. In the 6.4 kbps IMBE speech coder, which was standardized for the INMARSAT-M satellite communciation system, the 45 bits per frame which are reserved for forward error correction are divided among [23,12] Golay codes which can correct up to 3 errors, [15,11] Hamming codes which can correct single errors and parity bits. The six most significant bits from the fundamental frequency and the three most significant bits from the mean of the PRBA vector are first combined with three parity check bits and then encoded in a [23,12] Golay code. A second Golay code is used to encode the three most significant bits from the PRBA vector and the nine most sensitive bits from the higher order DCT coefficients. All of the remaining bits except the seven least sensitive bits are then encoded into five [15,11] Hamming codes. The seven least significant bits are not protected with error correction codes. Prior to transmission the 128 bits which represent a particular speech segment are interleaved such that at least five bits separate any two bits from the same code word. This feature spreads the effect of short burst errors over several different codewords, thereby increasing the probability that the errors can be corrected. At the decoder the received bits are passed through Golay and Hamming decoders which attempt to remove any bit errors from the data bits. The three parity check bits are checked and if no uncorrectable bit errors are detected then the received bits are used to reconstruct the MBE model parameters for the current frame. Otherwise if an uncorrectable bit error is detected then the received bits for the current frame are ignored and the model parameters from the previous frame are repeated for the current frame. The use of frame repeats has been found to improve the perceptual quality of the speech when bit errors are present. Thus, we examine each frame of received bits and determine whether the current frame is likely to contain a large number of uncorrectable bit errors. One method used to detect uncorrectable bit errors is to check extra parity bits which are inserted in the data. Thus, we also determine whether a large burst of bits errors has been encountered by comparing the number of correctable bit errors with the local estimate of the error rate. If the number of correctable bit errors is substantially greater than the local estimate of the error rate then a frame repeat is performed. Additionally, we check each frame for invalid bit sequences (i.e. groups of bits which the encoder never transmits). If an invalid bit sequence is detected a frame repeat is performed. The Golay and Hamming decoders also provide information on the number of correctable bit errors in the data. This information is used by the decoder to estimate the bit error rate. The estimate of the bit error rate is used to control adaptive smoothers which increase the perceived speech quality in the presence of uncorrectable bit errors. In addition the estimate of the error rate can be used to perform frame repeats in bad error environments. This aspect of the invention can be used with soft-decision coding to further improve performance. Soft-decision decoding uses additional information on the likelihood of each bit being in error to improve the error correction and detection capabilities of many different codes. Since this additional information is often available from a demodulator in a digital communication system, it can provide improved robustness to bit errors without requiring additional bits for error protection. We use a new frequency domain parameter enhancement method which improves the quality of synthesized speech. We first locate the perceptually important regions of the speech spectrum. We then increase the amplitude of the perceptually important frequency regions relative to other frequency regions. The preferred method for performing frequency domain parameter enhancement is to smooth the spectral envelope to estimate the general shape of the spectrum. The spectrum can be smoothed by fitting a low-order model such as an all-pole model, a cepstral model, or a polynomial model to the spectral envelope. The smoothed spectral envelope is then compared against the unsmoothed spectral envelope and perceptually important spectral regions are identified as regions where the unsmoothed spectral envelope has greater energy than the smoothed spectral envelope. Similarly regions where the unsmoothed spectral envelope has less energy than the smoothed spectral envelope are identified as perceptually less important. Parameter enhancement is performed by increasing the amplitude of perceptually important frequency regions and decreasing the amplitude of perceptually less important frequency regions. This new enhancement method increases speech quality by eliminating or reducing many of the artifacts which are introduced during the estimation and quantization of the speech parameters. In addition this new method improves the speech intelligibility by sharpening the perceptually important speech formants. In the IMBE speech decoder a first-order all-pole model is fit to the spectral envelope for each frame. This is done by estimating the correlation parameters. R 0 and R 1 from the decoded model parameters according to the following equations. where M and l for 1 ≤ l ≤ L and are the decoded spectral amplitudes for the current frame, and ω and 0 is the decoded fundamental frequency for the current frame. The correlation parameters R 0 and R 1 can be used to estimate a first-order all-pole model. This model is evaluated at the frequencies corresponding to the spectral amplitudes for the current frame (i.e. k ·ω and 0 for 1 ≤ l ≤ L and ) and used to generate a set of weights W l according to the following formula. These weights indicate the ratio of the smoothed all-pole spectrum to the IMBE spectral amplitudes. They are then used to individually control the amount of parameter enhancement which is applied to each spectral amplitude. This relationship is expressed in the following equation, where M and l for 1 ≤ l ≤ L are the enhanced spectral amplitudes for the current frame. The enhanced spectral amplitudes are then used to perform speech synthesis. The use of the enhanced model parameters improves speech quality relative to synthesis from the unenhanced model parameters. Further description of a particular embodiment of speech coding system employing this invention can be found in the document entitled "INMARSAT M Voice Codec", a copy of which has been placed in the file of this Application.

Claims: A method of encoding speech wherein the speech is broken into segments, each of said segments representing one of a succession of time intervals and having a spectrum of frequencies, and for each segment the spectrum is sampled at a set of frequencies to form a set of actual spectral amplitudes, with the frequencies at which the spectrum is sampled generally differing from one segment to the next, and wherein the spectral amplitudes for at least one previous segment are used to produce a set of predicted spectral amplitudes for a current segment, and wherein a set of prediction residuals for the current segment based on a difference between the actual spectral amplitudes for the current segment and the predicted spectral amplitudes for the current segment are used in subsequent encoding, characterized in that the predicted spectral amplitudes for the current segment are based at least in part on interpolating the spectral amplitudes of a previous segment to estimate the spectral amplitudes in the previous segment at the frequencies of the current segment. The method of encoding speech according to claim 1, further characterized in that the prediction residuals for a segment are grouped into blocks, an average of the prediction residuals within each block is determined, the averages of each of the blocks are grouped into a prediction residual block average (PRBA) vector, and the PRBA vector is encoded. The method of Claim 2, wherein there are a predetermined number of blocks, with the number of blocks being independent of the number of prediction residuals grouped into particular blocks. The method of Claim 3, wherein the predicted spectral-amplitudes for the current segment are based at least in part on interpolating the spectral amplitudes of a previous segment to estimate the spectral amplitudes in the previous segment at the frequencies of the current segment. The method of Claims 1 or 2, wherein the difference between the actual spectral amplitudes for the current segment and the predicted spectral amplitudes for the current segment is formed by subtracting a fraction of the predicted spectral amplitudes from the actual spectral amplitudes. The method of Claims 1 or 2, wherein the spectral amplitudes are obtained using a Multiband Excitation speech model. The method of Claims 1 or 2, wherein only spectral amplitudes from the most recent previous segment are used in forming the predicted spectral amplitudes of the current segment. The method of Claims 1 or 2, wherein said spectrum comprises a fundamental frequency and the set of frequencies for a given segment are multiples of the fundamental frequency of the segment. The method of Claims 3 or 4, wherein the number of prediction residuals in a lower frequency block is not larger than the number of prediction residuals in a higher frequency block. The method of any one of Claims 3, 4 or 9, wherein the number of blocks is equal to six (6). The method of Claim 10, wherein the difference between the number of elements in the highest frequency block and the number of elements in the lowest frequency block is less than or equal to one. The method of any one of Claims 2 to 4, wherein said average is computed by adding the prediction residuals within the block and dividing by the number of prediction residuals within that block. The method of Claim 12, wherein said average is obtained by computing a Discrete Cosine Transform (DCT) of the spectral amplitude prediction residuals within a block and using the first coefficient of the DCT as the average. The method of any one of Claims 2 to 4, wherein encoding the PRBA vector comprises vector quantizing the PRBA vector. The method of Claim 14, wherein said vector quantization is performed using a method comprising the steps of: determining an average of the PRBA vector; quantizing said average using scalar quantization; subtracting said average from the PRBA vector to form a zero-mean PRBA vector; and quantizing said zero-mean PRBA vector using vector quantization with a zero-mean code-book. The method of claim 2, 3 or 4, wherein the PRBA vector is encoded using a linear transform on the PRBA vector and scalar quantizing the transform coefficients. The method of claim 16, wherein said linear transform comprises a Discrete Cosine Transform.

Inventors: HARDWICK JOHN C, LIM JAE S, HARDWICK, JOHN C., LIM, JAE S.
Applicants: DIGITAL VOICE SYSTEMS INC, DIGITAL VOICE SYSTEMS, INC.
Application year: 1991
Main classification: G10L19
IPC codes: G10L19, H03M13, G10L21