Efficient vector quantization of LPC parameters at 24 bits/frame

Abstract

Linear prediction 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 application, it is important to quantize these parameters accurately using as few bits as possible without sacrificing the speech quality. Though the vector quantizers are more efficient than the scalar quantizers, their use for fine 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 is divided into two parts and each part is vector-quantized separately. The splitting of LPC vector is studied in the following three domains: (1) line spectral-pair frequency (LSF), (2) arc-sine reflection coefficient, and (3) log area ratio. Splitting in LSF domain is found to be the best. Using the localized spectral properties 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 1-dB average spectral distortion. In terms of average spectral distortion and number of outliers, its performance is better than the 32 bits/frame scalar quantizer.