A sampling-based speaker clustering using utterance-oriented Dirichlet process mixture model and its evaluation on large-scale data

APSIPA Transactions on Signal and Information Processing

Atsushi Nakamura³  Tetsunori Kobayashi¹  Shinji Watanabe²  Naohiro Tawara¹  Tetsuji Ogawa¹ 

[1] Waseda University;Naohiro Tawara;Nagoya City University

关键词: Sampling approach;    Non-parametric Bayesian model;    Gibbs sampling;    Utterance-oriented Dirichlet process mixture model;    Speaker clustering;   

DOI  :  10.1017/ATSIP.2015.19

学科分类：计算机科学（综合）

来源: Cambridge University Press

【 摘 要 】

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.

【 授权许可】

Unknown