Vector quantization of speech and speech-like waveforms

An algorithm for the design of vector quantizers that are locally optimum in the sense of minimizing an average quantitative distortion measure is used to design 1 and 2 bit/sample vector quantizers for both real sampled speech and a simulated speech-like auto-regressive random process. Both weighted and unweighted squared-error distortion measures are considered. Several comparisons are made and discussed based on the average distortions of the vector quantization schemes. The results for the simulated speech are compared to mathematical performance bounds from information theory to provide an indication of how nearly globally optimal vector quantization is for such highly correlated sources. A comparison of the results for the real speech and the simulated speech provides a quantitative measure of the accuracy of such models and, hence, of the applicability of information theory bounds and code designs based on probabilistic models. The signal-to-quantization-noise ratios of vector quantizers designed to minimize squared-error distortion are compared to those of several popular speech waveform coding systems of similar rates.