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Journal of Information Science and Engineering, Vol. 24 No.
6, pp. 1873-1886 (November 2008)
Speaker Clustering Based on Bayesian Information Criterion*
Wei-Ho Tsai
Department of Electronic Engineering
Graduate Institute of Computer and Communication Engineering
National Taipei University of Technology
Taipei, 106 Taiwan
This paper presents an effective method for clustering unknown speech utterances
based on their associated speakers. The proposed method jointly optimizes the generated
clusters and the number of clusters according to a Bayesian information criterion (BIC).
The criterion assesses a partitioning of utterances based on how high the level of withincluster
homogeneity can be achieved at the expense of increasing the number of clusters.
Unlike the existing methods, in which BIC is used only to determine the optimal number
of clusters, the proposed method uses BIC in conjunction with a genetic algorithm to determine
the optimal cluster where each utterance should be located at. The experimental
results show that the proposed speaker-clustering method outperforms the conventional
methods.
Keywords:
speaker clustering, Bayesian information criterion, genetic algorithm, Gaussian
mixture modeling, divergency
Retrieve PDF document (200811_16.pdf)
Received February 26, 2007; revised August 10, 2007; accepted October 24, 2007.
Communicated by Chin-Teng Lin.
* This paper was supported in part by the National Science Council of Taiwan, R.O.C. under grant No. NSC
95-2218-E-027-020. Part of this paper has been presented in the European Conference on Speech Communication
and Technology, Aug 27-31, 2007, Antwerp, Belgium, ISCA.