A weighted cepstral distance measure for speech recognition

Abstract

A weighted cepstral distance measure is proposed and is tested in a speaker-independent isolated word recognition system using standard DTW (Dynamic Time Warping) techniques. The measure is a statistically weighted distance measure with weights equal to the inverse variance of the cepstral coefficients. The experimental results show that the weighted cepstral distance measure works substantially better than both the Euclidean cepstral distance and the log likelihood ratio distance measures across two different data bases, namely a 10 digits and a 129 airline vocabulary words. The recognition accuracy obtained using the weighted cepstral distance measure was about 992 for digit recognition. This result was more than 3% higher than that obtained using the simple Euclidean cepstral distance measure and about 2% higher than the results using the log likelihood ratio distance measure. The most significant performance characteristic of the weighted cepstral distance was that it tended to equalize the performance of the recognizer across different talkers.