A sampling-based speaker clustering using utterance-oriented Dirichlet process mixture model and its evaluation on large-scale data
Naohiro Tawara, Tetsuji Ogawa, Shinji Watanabe, Atsushi Nakamura, Tetsunori Kobayashi · APSIPA Transactions on Signal and Information Processing · 2015
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.