Fully Bayesian inference of multi-mixture Gaussian model and its evaluation using speaker clustering

    研究成果: Conference contribution

    7 引用 (Scopus)

    抜粋

    This study aims to verify effective optimization methods for estimating parametric, fully Bayesian models in speech processing. For that purpose, we investigate the impact of the difference in optimization methods for the multi-scale Gaussian mixture model, which is suitable for speaker clustering, on the clustering accuracy. The Markov chain Monte Carlo (MCMC)-based method was compared with the variational Bayesian method in the speaker clustering experiment; with a small amount of data, the MCMC-based method was more effective; with large scale data (more than one million samples), the difference between these methods in terms of the clustering accuracy decreased and the MCMC-based method was computationally efficient.

    元の言語English
    ホスト出版物のタイトルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    ページ5253-5256
    ページ数4
    DOI
    出版物ステータスPublished - 2012
    イベント2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto
    継続期間: 2012 3 252012 3 30

    Other

    Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
    Kyoto
    期間12/3/2512/3/30

      フィンガープリント

    ASJC Scopus subject areas

    • Signal Processing
    • Software
    • Electrical and Electronic Engineering

    これを引用

    Tawara, N., Ogawa, T., Watanabe, S., & Kobayashi, T. (2012). Fully Bayesian inference of multi-mixture Gaussian model and its evaluation using speaker clustering. : ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 5253-5256). [6289105] https://doi.org/10.1109/ICASSP.2012.6289105