Partly Hidden Markov Model and its application to speech recognition

Tetsunori Kobayashi, Junko Furuyama, Ken Masumitsu

    Research output: Chapter in Book/Report/Conference proceedingChapter

    9 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    PublisherIEEE
    Pages121-124
    Number of pages4
    Volume1
    Publication statusPublished - 1999
    EventProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA
    Duration: 1999 Mar 151999 Mar 19

    Other

    OtherProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99)
    CityPhoenix, AZ, USA
    Period99/3/1599/3/19

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    ASJC Scopus subject areas

    • Signal Processing
    • Electrical and Electronic Engineering
    • Acoustics and Ultrasonics

    Cite this

    Kobayashi, T., Furuyama, J., & Masumitsu, K. (1999). Partly Hidden Markov Model and its application to speech recognition. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 1, pp. 121-124). IEEE.