This paper provides the analytical solution and algorithm of UO-DPMM based on a non-parametric Bayesian manner, and thus realizes fully Bayesian speaker clustering. We carried out preliminary speaker clustering experiments by using a TIMIT database to compare the proposed method with the conventional Bayesian Information Criterion (BIC) based method, which is an approximate Bayesian approach. The results showed that the proposed method outperformed the conventional one in terms of both computational cost and robustness to changes in tuning parameters.
Bibliographic reference. Tawara, Naohiro / Watanabe, Shinji / Ogawa, Tetsuji / Kobayashi, Tetsunori (2011): "Speaker clustering based on utterance-oriented dirichlet process mixture model", In INTERSPEECH-2011, 2905-2908.