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2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
ページ5253-5256
ページ数4
DOI
出版物ステータスPublished - 2012 10 23
イベント2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
継続期間: 2012 3 252012 3 30

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(印刷物)1520-6149

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Japan
Kyoto
期間12/3/2512/3/30

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • 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. : 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 5253-5256). [6289105] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6289105