Temporal Hidden Markov Models

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    1 Citation (Scopus)

    Abstract

    The hidden Markov model (HMM) is a double stochastic process. The observable process produces a sequence of observations and the hidden process is a Markov process. The HMM assumes that the occurrence of one observation is statistically independent of the occurrence of the others. To avoid this limitation, a temporal HMM is proposed. The hidden process in the temporal HMM is the same, but the observable process is now a Markov process. Each observation in the training sequence is assumed to be statistically dependent on its predecessor, and codewords or Gaussian components are used as states in the observable Markov process. Speaker identification experiments performed on 138 Gaussian mixture speaker models in the YOHO database shows a better performance for the temporal HMM compared to the standard HMM
    Original languageEnglish
    Title of host publicationProceedings of the 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing
    EditorsJ Kwok, LM Po
    Place of PublicationHong Kong
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages137-140
    Number of pages4
    ISBN (Print)0-7803-8688-4
    Publication statusPublished - 2004
    Event2004 International Symposium on Intelligent Multimedia, Video and Speech Processing - , Hong Kong
    Duration: 19 Oct 200421 Oct 2004

    Conference

    Conference2004 International Symposium on Intelligent Multimedia, Video and Speech Processing
    CountryHong Kong
    Period19/10/0421/10/04

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  • Cite this

    Tran, D. (2004). Temporal Hidden Markov Models. In J. Kwok, & LM. Po (Eds.), Proceedings of the 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing (pp. 137-140). IEEE, Institute of Electrical and Electronics Engineers.