Enhancement of speech coding in background noise for low-rate speech coder

Abstract

A speech coding system employs measurements of robust features of speech frames whose distribution are not strongly affected by noise/levels to make voicing decisions for input speech occurring in a noisy environment. Linear programing analysis of the robust features and respective weights are used to determine an optimum linear combination of these features. The input speech vectors are matched to a vocabulary of codewords in order to select the corresponding, optimally matching codeword. Adaptive vector quantization is used in which a vocabulary of words obtained in a quiet environment is updated based upon a noise estimate of a noisy environment in which the input speech occurs, and the “noisy” vocabulary is then searched for the best match with an input speech vector. The corresponding clean codeword index is then selected for transmission and for synthesis at the receiver end. The results are better spectral reproduction and significant intelligibility enhancement over prior coding approaches. Robust features found to allow robust voicing decisions include: low-band energy; zero-crossing counts adapted for noise level; AMDF ratio (speech periodicity) measure; low-pass filtered backward correlation; low-pass filtered forward correlation; inverse-filtered backward correlation; and inverse-filtered pitch prediction gain measure.

I claim:  
     
       1. In a method of low-bit-rate speech coding of input speech occurring in a noisy environment, for a system which employs linear predictive coding (LPC) analysis of input speech frames to generate reflection coefficients, conversion of the reflection coefficients to vectors representing spectral parameters of the input speech frames, and matching of the spectral parameter vectors against reference vectors of a vocabulary of codewords generated in a training sequence in order to select the corresponding index of an optimally matching codeword for transmission, 
       the improvement comprising the steps of:  
       selecting a set of at least two features which are characterized by a probability distribution which is not strongly affected in the noisy environment and which allow discrimination between voiced and unvoiced input speech, wherein said selected features include the feature of zero-crossing counts which are based on average noise energy;  
       measuring the selected features for input speech frames; and  
       using said feature measurements to make voiced/unvoiced speech decisions in order to select the voice/unvoiced excitation for speech synthesis in the receiver;  
       using noise estimates to update the reference vectors of the vocabulary of codewords, wherein new reference vectors are generated corresponding to said vocabulary of codewords in the noisy environment, said noise estimates including noise amplitude and noise reflection coefficients, wherein said noise estimate for speech frame I is performed only if the ith speech frame is unvoiced and more than a given number L of continuous unvoiced speech frames are accumulated, in order to prevent using voiced or unvoiced speech in the noise estimate.  
     
     
       2. A low-bit-rate speech coding method according to  claim 1 , wherein said voicing decision step includes the substep of determining a linear combination of said features which provides a high voiced/unvoiced discrimination capability; and determining respective weights to be applied to said features in order to obtain an optimal linear combination of said features. 
     
     
       3. A low-bit-rate speech coding method according to  claim 2 , wherein said weights determining substep of said voicing decision step is performed using the simplex method for obtaining a maximum quantity h for an average distance between voiced and unvoiced regions of the input speech. 
     
     
       4. A low-bit-rate speech coding method according to  claim 1 , wherein said selected features include the feature of low-band energy. 
     
     
       5. A low-bit-rate speech coding method according to  claim 1 , wherein said selected features include an AMDF ratio (speech periodicity) measure. 
     
     
       6. A low-bit-rate speech coding method according to  claim 1 , wherein said selected features include a backward correlations measure responsive to low-pass-filtered speech energy. 
     
     
       7. A low-bit-rate speech coding method according to  claim 1 , wherein said selected features include a forward correlations measure responsive to low-pass-filtered speech energy. 
     
     
       8. A low-bit-rate speech coding method according to  claim 1 , wherein said selected features include a backward correlations measure responsive to inverse-filtered speech energy. 
     
     
       9. A low-bit-rate speech coding method according to  claim 1 , wherein said selected features include a pitch prediction gain measure responsive to inverse-filtered speech energy. 
     
     
       10. A low-bit-rate speech coding method according to  claim 1 , adapted for the environment of helicopter noise, and further comprising the step of low-pass filtering of speech energy at a cutoff frequency of about 420 Hz. 
     
     
       11. A low-bit-rate speech coding method according to  claim 10 , wherein said LPC analysis is conducted as 14th-order LPC analysis. 
     
     
       12. In a method of low-bit-rate speech coding of input speech occurring in a noisy environment, for a system which employs linear predictive coding (LPC) analysis of input speech frames to generate reflection coefficients, conversion of the reflection coefficients to vectors representing spectral parameters of the input speech frames, and matching of the spectral parameter vectors against reference vectors of a vocabulary of codewords generated in a training sequence in order to select the corresponding index of an optimally matching codeword for transmission, 
       the improvement comprising the steps of:  
       selecting a set of features which are characterized by a probability distribution which is not strongly affected in the noisy environment and which allow discrimination between voiced and unvoiced input speech;  
       measuring the selected features for input speech frames; and  
       using said feature measurements to make voiced/unvoiced speech decisions in order to select the voice/unvoiced excitation for speech synthesis in the receiver;  
       using noise estimates to update the reference vectors of the vocabulary of codewords, wherein new reference vectors are generated corresponding to said vocabulary of codewords in the noisy environment, said noise estimates including noise amplitude and noise reflection coefficients, wherein said noise estimate for speech frame I is performed only if the ith speech frame is unvoiced and more than a given number L of continuous unvoiced speech frames are accumulated, in order to prevent using voiced or unvoiced speech in the noise estimate.  
     
     
       13. A low-bit-rate speech coding method according to  claim 12 , wherein the vocabulary of codewords is generated for speech in a quiet environment, said quiet environment vocabulary is updated with noise estimates to obtain a vocabulary of codewords corresponding to the noisy environment, said noisy environment vocabulary constituting said reference vectors against which said spectral parameter vectors are matched, and speech is synthesized at a received end of the speech coding system using said quiet environment vocabulary. 
     
     
       14. A method for speech coding for transmission comprising: 
       
         providing a first vector quantization codebook of reference vectors, each reference vector representing spectral characteristics of a corresponding time frame of a reference speech signal;  
       
       
         accepting an input audio signal;  
       
       
         updating the reference vectors of the first vector quantization codebook to produce a corresponding second vector quantization codebook, including updating each reference vector to represent spectral characteristics of a combination of the corresponding time frame of the reference speech signal and a background noise present in the input audio signal; and  
       
       
         quantizing a time frame of the input audio signal according to the second vector quantization codebook, selecting the most similar updated reference vector to spectral characteristics of said time frame of the input audio signal. 
       
     
     
       15. The method of  claim 14  further comprising: 
       
         transmitting an index of a reference vector of the first vector quantization codebook that corresponds to the selected updated reference vector of the second vector quantization codebook;  
       
       
         receiving the transmitted index; and  
       
       
         synthesizing a time frame of an output signal, including selecting a reference vector of the first vector quantization codebook according to the received index. 
       
     
     
       16. The method of  claim 14  further comprising estimating a characteristic of the background noise present in the input audio signal. 
     
     
       17. The method of  claim 16  further comprising repeating the steps of estimating the background noise of the input audio signal, updating the reference vectors of the first vector quantization codebook and quantizing the input signal according to the second vector quantization codebook for each of a sequence of time frames of the input audio signal. 
     
     
       18. The method of  claim 16  wherein estimating a characteristic of the background noise includes: 
       
         determining any of a consecutive series of a predetermined number of time frames of the input audio signal includes voiced speech; and  
       
       
         if none of the series of time frames includes voiced speech, estimating the characteristic of the background noise in at least one of said series of time frames. 
       
     
     
       19. The method of  claim 18  wherein estimating the characteristic of the background noise in at least one of said series of time frames includes determining an average noise amplitude in said time frame. 
     
     
       20. The method of  claim 18  wherein estimating the characteristic of the background noise in at least one of said series of time frames includes determining a spectral representation of the background noise. 
     
     
       21. The method of  claim 20  wherein determining the spectral representation of the background noise includes determining autocorrelation coefficients of the background noise. 
     
     
       22. The method of  claim 16  wherein: 
       
         estimating the characteristic of the background noise includes determining autocorrelation coefficients of the background noise; and  
       
       
         updating each reference vector includes determining an autocorrelation coefficient representation of the spectral characteristics associated with the reference vector, and combining said autocorrelation coefficients with the autocorrelation coefficients of the background noise to produce an autocorrelation coefficient representation of the spectral characteristics represented by the updated reference vector. 
       
     
     
       23. The method of  claim 22  wherein updating each reference vector further includes computing a line spectral frequency ( LSF )  representation from the autocorrelation coefficients associated with each of the updated reference vectors, and quantizing the time frame of the input audio signal includes comparing a LSF representation of the time frame of the input signal with the LSF representation of the updated reference vectors.   
     
     
       24. A speech encoder comprising: 
       
         an input processor for accepting and input audio signal;  
       
       
         a background noise estimator coupled to the input processor for estimating a characteristic of a background noise present in the input audio signal;  
       
       
         a first vector quantization codebook including reference vectors, each reference vector representing spectral characteristics of a corresponding time frame of a reference speech signal;  
       
       
         a second vector quantization codebook of updated reference vectors, each updated reference vector corresponding to a different one of the reference vectors of the first vector quantization codebook, and each reference vector representing spectral characteristics of a combination of the corresponding time frame of the reference speech signal and the background noise present in the input audio signal;  
       
       
         a codebook updater coupled to the first vector quantization codebook, the background noise estimator, and the second vector quantization codebook, configured to accept reference vectors of the first vector quantization codebook and the characteristic of the background noise and produce the reference vectors of the second vector quantization codebook;  
       
       
         a vector quantizer coupled to the second quantization codebook and the input processor configured to quantize a time frame of the input audio signal according to the second vector quantization codebook by selecting the most similar reference vector of said second codebook to spectral characteristics of said time frame of the input audio signal; and  
       
       
         a transmitter coupled to the vector quantizer to sending an index of the reference vector of the first vector quantization codebook that corresponds to the selected most similar reference vector of the second vector quantization codebook.