Efficient Vector Quantization of LPC Parameters at 24 Bits/Frame

Linear predictive coding (LPC) parameters are widely used in various speech processing applications for representing the spectral envelope information of speech. For low bit rate speech-coding applications, it is important to quantize these parameters accurately using as few bits as possible. Though the vector quantizers are more efficient than the scalar quantizers, their use for accurate quantization of LPC information (using 24-26 bits/frames) is impeded due to their prohibitively high complexity. In this paper, a split vector quantization approach is used to overcome the complexity problem. Here, the LPC vector consisting of 10 line spectral frequencies (LSF's) is divided into two parts and each part is quantized separately using vector quantization. Using the localized spectral sensitivity property of the LSF parameters, a weighted LSF distance measure is proposed. Using this distance measure, it is shown that the split vector quantizer can quantize LPC information in 24 bits/frame with an average spectral distortion of 1 dB and less than 2% frames having spectral distortion greater than 2 dB. Effect of channel errors on the performance of this quantizer is also investigated and results are reported.