Stochastic Gaussian model for low-bit rate coding of LPC area parameters

Abstract

Accurate quantization of LPC parameters with a minimum number of bits is necessary for synthesizing high quality speech at low bit rates. Earlier work by Juang and Gray has shown that vector quantization can provide a significant reduction in the bit rate needed to quantize the LPC parameters. Previous work on vector quantization of LPC parameters employed trained codebooks. In this paper, we describe a stochastic model of LPC-derived log areas that eliminates training of the codebook by constructing codebook entries from random sequences. Each vector of LPC parameters is modelled as a sample function of zero mean Gaussian stochastic process with known covariances. We generate an ensemble of Gaussian codewords with a specified distribution where the number of codewords in the ensemble is determined by the number of bits used to quantize the LPC parameter vector. The optimum codeword is selected by exhaustive search to minimize the Euclidean distance between the original and quantized parameters. Our results show that vector quantization using random codebooks can provide a SNR of 20 dB in quantizing 10 log area parameters with 28 bits/frame. An important advantage of random codebook is that they provide robust performance across different speakers and speech recording conditions.