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Speaker Identification with Estimating the Number of Cluster Based on Boundary Subtractive Clustering  

Lee, Youn-Jeong (국방과학연구소)
Choi, Min-Jung ((주)인스모바일 기술연구소)
Seo, Chang-Woo ((주)에스씨디정보통신연구소)
Hahn, Hern-Soo (숭실대학교 정보통신전자공학부)
Publication Information
The Journal of the Acoustical Society of Korea / v.26, no.5, 2007 , pp. 199-206 More about this Journal
Abstract
In this paper we propose a new clustering algorithm that performs clustering the feature vectors for the speaker identification. Unlike typical clustering approaches, the proposed method performs the clustering without the initial guesses of locations of the cluster centers and a priori information about the number of clusters. Cluster centers are obtained incrementally by adding one cluster center at a time through the boundary subtractive clustering algorithm. The number of clusters is obtained from investigating the mutual relationship between clusters. The experimental results for artificial datum and TIMIT DB show the effectiveness of the proposed algorithm as compared with the conventional methods.
Keywords
Speaker identification; Clustering; Subtractive clustering; K-means algorithm; Mutual relationship;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 Lozano, J.A, Pena, J.M., and Larranaga, P., 'An empirical comparison of four initialization methods for the k-means algorithm,' Pattern Recognition Letters, 20 1027-1040, 1999   DOI   ScienceOn
2 Gath and Geva, A.B., 'Unsupervised optimal fuzzy clustering,' IEEE Trans. Pattern and Machine Intelligence, 11 (7) 773-778, 1989   DOI   ScienceOn
3 Ryager, R., Filev, D.P., 'Approximate clustering via the mountain method,' IEEE Trans on Systems, Man, and Cybernetics, 24, 1279-1284, 1994   DOI   ScienceOn
4 Likas, A, Vlassis, N. and Verbeek, J.J., 'The global k-means clustering algorithm,' Pattern recognition 36, 451-461, 2003   DOI   ScienceOn
5 Lee, Y., Lee, J. and Lee, K.Y, 'The Estimating Optimal Number of Gaussian Mixtures Based on Incremental k-means for Speaker Identification,' International Journal of Information Technology, 12 (7) 2006
6 Zadeh, L.A, 'Similarity Relations and fuzzy Orderings,' Information Science 3, 177-200, 1971   DOI   ScienceOn
7 Chiu, S.L., 'Fuzzy model identification based on cluster estimation,' J. of Intelligent and Fuzzy sys., 2 (3), 267-278, 1994
8 Kothari, R., Pittas, D., 'On finding the number of clusters,' Pattern Recognition Letters 20, 405-416, 1999   DOI   ScienceOn
9 Yang, Z. R. and Zwaliuski, M., 'Mutual information theory for adaptive mixture model,' IEEE Trans. Pattern and Machine Intelligence, 23 (4) 396-403, 2001   DOI   ScienceOn
10 이윤정, 서창우, 한헌수, 이기용, 'GMM을 위한 점진적 K-means 알고리즘에 의해 초기값을 갖는 EM알고리즘과 화지식별에의 적용,' 한국음향학회지, 24 (3) 117-126, 2005   과학기술학회마을