Speaker clustering based on utterance-oriented Dirichlet process mixture model

    研究成果: Conference contribution

    6 引用 (Scopus)

    抜粋

    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.

    元の言語English
    ホスト出版物のタイトルProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
    ページ2905-2908
    ページ数4
    出版物ステータスPublished - 2011
    イベント12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy
    継続期間: 2011 8 272011 8 31

    Other

    Other12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011
    Italy
    Florence
    期間11/8/2711/8/31

      フィンガープリント

    ASJC Scopus subject areas

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

    これを引用

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