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

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

    7 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    Pages5253-5256
    Number of pages4
    DOIs
    Publication statusPublished - 2012
    Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto
    Duration: 2012 Mar 252012 Mar 30

    Other

    Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
    CityKyoto
    Period12/3/2512/3/30

    Fingerprint

    Markov processes
    Speech processing
    Experiments

    Keywords

    • Gibbs sampling
    • multi-scale Gaussian mixture model
    • Speaker clustering
    • variational Bayesian method

    ASJC Scopus subject areas

    • Signal Processing
    • Software
    • Electrical and Electronic Engineering

    Cite this

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

    Fully Bayesian inference of multi-mixture Gaussian model and its evaluation using speaker clustering. / Tawara, Naohiro; Ogawa, Tetsuji; Watanabe, Shinji; Kobayashi, Tetsunori.

    ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2012. p. 5253-5256 6289105.

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

    Tawara, N, Ogawa, T, Watanabe, S & Kobayashi, T 2012, Fully Bayesian inference of multi-mixture Gaussian model and its evaluation using speaker clustering. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings., 6289105, pp. 5253-5256, 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012, Kyoto, 12/3/25. https://doi.org/10.1109/ICASSP.2012.6289105
    Tawara N, Ogawa T, Watanabe S, Kobayashi T. Fully Bayesian inference of multi-mixture Gaussian model and its evaluation using speaker clustering. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2012. p. 5253-5256. 6289105 https://doi.org/10.1109/ICASSP.2012.6289105
    Tawara, Naohiro ; Ogawa, Tetsuji ; Watanabe, Shinji ; Kobayashi, Tetsunori. / 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. 2012. pp. 5253-5256
    @inproceedings{4b0cdebe575d491aa99957eb361960b2,
    title = "Fully Bayesian inference of multi-mixture Gaussian model and its evaluation using speaker clustering",
    abstract = "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.",
    keywords = "Gibbs sampling, multi-scale Gaussian mixture model, Speaker clustering, variational Bayesian method",
    author = "Naohiro Tawara and Tetsuji Ogawa and Shinji Watanabe and Tetsunori Kobayashi",
    year = "2012",
    doi = "10.1109/ICASSP.2012.6289105",
    language = "English",
    isbn = "9781467300469",
    pages = "5253--5256",
    booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

    }

    TY - GEN

    T1 - Fully Bayesian inference of multi-mixture Gaussian model and its evaluation using speaker clustering

    AU - Tawara, Naohiro

    AU - Ogawa, Tetsuji

    AU - Watanabe, Shinji

    AU - Kobayashi, Tetsunori

    PY - 2012

    Y1 - 2012

    N2 - 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.

    AB - 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.

    KW - Gibbs sampling

    KW - multi-scale Gaussian mixture model

    KW - Speaker clustering

    KW - variational Bayesian method

    UR - http://www.scopus.com/inward/record.url?scp=84867626020&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84867626020&partnerID=8YFLogxK

    U2 - 10.1109/ICASSP.2012.6289105

    DO - 10.1109/ICASSP.2012.6289105

    M3 - Conference contribution

    AN - SCOPUS:84867626020

    SN - 9781467300469

    SP - 5253

    EP - 5256

    BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

    ER -