A sampling-based speaker clustering using utterance-oriented Dirichlet process mixture model and its evaluation on large-scale data
Keyword(s):
An infinite mixture model is applied to model-based speaker clustering with sampling-based optimization to make it possible to estimate the number of speakers. For this purpose, a framework of non-parametric Bayesian modeling is implemented with the Markov chain Monte Carlo and incorporated in the utterance-oriented speaker model. The proposed model is called the utterance-oriented Dirichlet process mixture model (UO-DPMM). The present paper demonstrates that UO-DPMM is successfully applied on large-scale data and outperforms the conventional hierarchical agglomerative clustering, especially for large amounts of utterances.
2012 ◽
2015 ◽
Vol 9
(7)
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pp. 773-781
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2016 ◽
Vol 17
(9)
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pp. 2594-2599
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