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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

Full Text () 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.