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

概要

論文の詳細を見る
A new speech recognition system using the neurally-inspired Learning Vector Quantization (LVQ) to train 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 new algorithm descirbed here combines the advantages inherent in each of these two algorithms. Instead of using a conventional, K-means generated codebook in the HMMs, the new system uses LVQ to adapt the codebook reference vectors so as to minimize the number of errors these reference vectors make when used for nearest neighbor classification of training vectors. The LVQ codebook can then provide the HMMs with high classification power at the phonemic level. As the results of phoneme recognition experiments using a large vocabulary database of 5, 240 common Japanese words uttered in isolation by a male speaker, it was confirmed that the high discriminant ability of LVQ could be integrated into an HMM architecture easily extendible to longer utterance models, such as word or sentence models.
社団法人日本音響学会の論文