Generalization of state-observation-dependency in Partly Hidden Markov Models

研究成果: Paper査読

3 被引用数 (Scopus)

抄録

Generalized Partly Hidden Markov Model (GPHMM) is proposed by modifying Partly Hidden Markov Model (PHMM), and it is successfully applied to the speech recognition. PHMM, which was proposed in our previous paper, is the novel stochastic model, in which the pairs of the hidden states (H-state) and the observable states (O-state) determine the stochastic phenomena of the current observation and the next state transition. In the formulation of PHMM, we used common pair of H-state and O-state to determine both of these phenomena. In the formulation of GPHMM proposed here, we use common H-state but different O-states for the current observation and for the next state transition separately. This slight modification brought the big flexibility in the modeling of phenomena. Experimental results showed the effectiveness of GPHMM (without delta parameters): it reduced the word error by 17% compared to triphone HMM (with delta parameters), respectively.

本文言語English
ページ2673-2676
ページ数4
出版ステータスPublished - 2002 1 1
イベント7th International Conference on Spoken Language Processing, ICSLP 2002 - Denver, United States
継続期間: 2002 9 162002 9 20

Other

Other7th International Conference on Spoken Language Processing, ICSLP 2002
CountryUnited States
CityDenver
Period02/9/1602/9/20

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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