Title :
HMM continuous speech recognition using stochastic language models
Author :
Kita, Kenji ; Kawabaa, T. ; Hanazawa, Toshiyuki
Author_Institution :
ATR Interpreting Telephony Res. Lab., Kyoto, Japan
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
Keywords :
Markov processes; context-free grammars; natural languages; speech recognition; Japanese syllables; LR parsing; context-free rewriting rules; continuous speech recognition; fuzzy VQ; hidden Markov model; stochastic language models; stochastic shift/reduce model; trigram model; vector quantization; Cepstral analysis; Context modeling; Fuzzy systems; Hidden Markov models; Natural languages; Predictive models; Probability; Speech recognition; Stochastic processes; Stochastic systems; Vector quantization;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on
Conference_Location :
Albuquerque, NM
DOI :
10.1109/ICASSP.1990.115779
