A hybrid speech recognition system using HMMs with an LVQ-trained codebook

A speech recognition system using the neurally inspired learning vector quantization (LVQ) to train hidden Markov model (HMM) codebooks is described. Both LVQ and HMMs are stochastic algorithms holding considerable promise for speech recognition. In particular, LVQ is a vector quantizer with very powerful classification ability. HMMs, on the other hand, have the advantage that phone models can easily be concatenated to produce long utterance models, such as word or sentence models. The algorithm described combines the advantages inherent in each of these two algorithms. As the result of phoneme recognition experiments using a large vocabulary database of 5240 common Japanese words uttered in isolation by a male speaker, it is confirmed that the high discriminant ability of LVQ could be integrated into an HMM architecture easily extendible to longer utterance models