Fully Bayesian speaker clustering based on hierarchically structured utterance-oriented Dirichlet process mixture model

Naohiro Tawara, Tetsuji Ogawa, Shinji Watanabe, Atsushi Nakamura, Tetsunori Kobayashi

We proposed a novel Bayesian speaker clustering method based on a nonparametric Bayesian model which has a hierarchical structure. We carried out preliminary speaker clustering experiments with the conventional hierarchical agglomerative clustering based on Bayesian information criterion (AHC-BIC). Experimental result showed that the proposed method was effective to the data in which the number of utterances varied from speaker to speaker, while the conventional method caused significant degradation in clustering accuracy for these data.

Index Terms Speaker clustering, nonparametric Bayesian model, Gibbs sampling, utterance-oriented Dirichlet process mixture model.

doi: 10.21437/Interspeech.2012-578

Cite as: Tawara, N., Ogawa, T., Watanabe, S., Nakamura, A., Kobayashi, T. (2012) Fully Bayesian speaker clustering based on hierarchically structured utterance-oriented Dirichlet process mixture model. Proc. Interspeech 2012, 2166-2169, doi: 10.21437/Interspeech.2012-578

@inproceedings{tawara12_interspeech,
  author={Naohiro Tawara and Tetsuji Ogawa and Shinji Watanabe and Atsushi Nakamura and Tetsunori Kobayashi},
  title={{Fully Bayesian speaker clustering based on hierarchically structured utterance-oriented Dirichlet process mixture model}},
  year=2012,
  booktitle={Proc. Interspeech 2012},
  pages={2166--2169},
  doi={10.21437/Interspeech.2012-578}
}