HMM continuous speech recognition using stochastic language models

Abstract

Three stochastic language models are investigated for hidden Markov model (HMM) continuous-speech recognition system. They are the trigram model of Japanese syllables, the stochastic shift/reduce model in LR parsing, and the trigram model of context-free rewriting rules. These stochastic language models are incorporated into the HMM-LR continuous-speech recognition system. The phrase recognition rate is improved from 72.4% to 81.0%. Moreover, for a high-quality HMM-LR speech recognition system which uses separate vector quantization (VQ) and fuzzy VQ, the phrase recognition rate is improved from 88.2% to 93.2%, and a rate of 100% is achieved for the top four choices.< >