A Vector Quantizer Combining Energy and LPC Parameters and Its Application to Isolated Word Recognition

Abstract

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 modeled. In this paper we present a method of incorporating LPC spectral shape and energy into the code-book entries of the vector quantizer. To do this, we postulate a distortion measure for comparing two LPC vectors that uses the 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 found that a fairly significant correlation exists between LPC shape and signal energy. Hence, an LPC shape combined with energy vector quantizer with a given distortion requires far fewer code-book 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 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.