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

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages2673-2676
Number of pages4
Publication statusPublished - 2002 Jan 1
Event7th International Conference on Spoken Language Processing, ICSLP 2002 - Denver, United States
Duration: 2002 Sep 162002 Sep 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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    Ogawa, T., & Kobayashi, T. (2002). Generalization of state-observation-dependency in Partly Hidden Markov Models. 2673-2676. Paper presented at 7th International Conference on Spoken Language Processing, ICSLP 2002, Denver, United States.