Speaker clustering based on utterance-oriented Dirichlet process mixture model

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    6 Citations (Scopus)

    Abstract

    This paper provides the analytical solution and algorithm of UO-DPMM based on a non-parametric Bayesian manner, and thus realizes fully Bayesian speaker clustering. We carried out preliminary speaker clustering experiments by using a TIMIT database to compare the proposed method with the conventional Bayesian Information Criterion (BIC) based method, which is an approximate Bayesian approach. The results showed that the proposed method outperformed the conventional one in terms of both computational cost and robustness to changes in tuning parameters.

    Original languageEnglish
    Title of host publicationProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
    Pages2905-2908
    Number of pages4
    Publication statusPublished - 2011
    Event12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy
    Duration: 2011 Aug 272011 Aug 31

    Other

    Other12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011
    CountryItaly
    CityFlorence
    Period11/8/2711/8/31

      Fingerprint

    Keywords

    • Gibbs sampling
    • Non-parametric Bayesian model
    • Speaker clustering
    • Utterance-oriented DPMM

    ASJC Scopus subject areas

    • Language and Linguistics
    • Human-Computer Interaction
    • Signal Processing
    • Software
    • Modelling and Simulation

    Cite this

    Tawara, N., Watanabe, S., Ogawa, T., & Kobayashi, T. (2011). Speaker clustering based on utterance-oriented Dirichlet process mixture model. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (pp. 2905-2908)