Partly Hidden Markov Model and its application to speech recognition

Tetsunori Kobayashi*, Junko Furuyama, Ken Masumitsu


研究成果: Conference article査読

10 被引用数 (Scopus)


A new pattern matching method, Partly Hidden Markov Model, is proposed and applied to speech recognition. Hidden Markov Model, which is widely used for speech recognition, can deal with only piecewise stationary stochastic process. We solved this problem by introducing the modified second order Markov Model, in which the first state is hidden and the second one is observable. In this model, not only the feature parameter observations but also the state transitions are dependent on the previous feature observation. Therefore, even the complicated transient can be modeled precisely. Some simulational experiments showed the high potential of the proposed model. As the results of word recognition test, the error rate was reduced by 39% compared with normal HMM.

ジャーナルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
出版ステータスPublished - 1999 1月 1
イベントProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA
継続期間: 1999 3月 151999 3月 19

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学


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