Speech recognition using HMMs with an LVQ-trained codebook

Abstract

A new speech recognition algorithm using HMMs with a codebook trained by learning vector quantization (LVQ) is here described. Both HMMs and LVQ are stochastic algorithms holding considerable promise for speech recognition. In particular, HMMs have the significant advantage that phone models can easily be concatenated to produce a long utterance model, such as a word or sentence model. LVQ, on the other hand, is a powerful classifier, as shown in the high phoneme recognition rates obtained in McDermott and Katagiri (1989). The new algorithm described here combines the advantages inherent in each of these two algorithms. To evaluate this LVQ-HMM hybrid, phoneme recognition experiments were performed using the same data as used in TDNN (Waibel, 1988) and shift-tolerant LVQ (McDermott and Katagiri, 1989) experiments. Applied to various phoneme recognition tasks, the LVQ-HMM hybrid achieved recognition rates much higher than those of a conventional HMM with a codebook designed by K-means clustering. For small codebook sizes, the hybrid model realized a more than tenfold decrease in recognition error rates, yielding extremely high phoneme recognition performance, comparable to that of TDNN or shift-tolerant LVQ.