A new HMM/LVQ hybrid algorithm for speech recognition

Abstract

A new HMM/LVQ hybrid algorithm for speech recognition is proposed. The motivations are: (1) to expand the capability of learning vector quantization (LVQ) for handling dynamic speech patterns and (2) to improve the performance of an HMM-based system. It is shown that, by combining both the discriminative power of LVQ and the capability of modeling temporal variations of speech of an HMM into a hybrid algorithm, the performance of the original HMM-based speech recognition algorithm is significantly improved. The proposed recognition algorithm uses HMM to segment speech utterances and then adopts a novel classifier in place of the conventional HMM likelihood comparison for recognition. Since the parameters of the classifier can be estimated through adaptive learning rules, the discriminative power of the recognizer is greatly enhanced. Any learnable classifier, such as an artificial neural network (ANN), can be used for the discriminative classifier. By way of example, an LVQ-based multicategory classifier is used in this study. The LVQ codebook is obtained through a probabilistic descent method using segmented and normalized speech tokens as training samples. The evaluation was conducted using a multispeaker, isolated English E-set letter database. The average word accuracy for the original HMM-based system was 61.7%. When the LVQ classifier was incorporated into the hybrid algorithm, the word accuracy increased to 81.3%.