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The theory of vector quantization (VQ) of linear predictive coding (LPC) coefficients has established a wide variety of techniques for quantizing LPC spectral shape to minimize overall spectral distortion. Such vector quantizers have been widely used in the areas of speech coding and speech recognition. The conventional vector quantizer utilizes only spectral shape information and essentially disregards the energy or gain term associated with the optimal LPC fit to the signal being modelled. In this paper we present a method of incorporating LPC spectral shape and energy into the codebook entries of the vector quantizer. To do this we postulate a distortion measure for comparing two LPC vectors which uses a weighted sum of an LPC shape distortion and a log energy distortion. Based on this combined distortion measure we have designed and studied vector quantizers of several sizes for use in isolated word speech recognition experiments. We have found that a fairly significant correlation exists between LPC shape and signal energy; hence a combined LPC shape plus energy vector quantizer with a given distortion requires far fewer codebook entries than one in which LPC shape and energy are quantized separately. Based on isolated word recognition tests on both a 10-digit and a 129 word airlines vocabulary, we have found improvements in recognition accuracy by using the VQ with both LPC shape and energy over that obtained using a VQ with LPC shape alone.
Rabiner et al. (Thu,) studied this question.