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Cluster-based support vector machines in text-independent speaker identification

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5 Author(s)
Sheng-Yu Sun ; Dept. of Comput. Sci. & Inf. Eng., Nat. Chiao-Tung Univ., Hsinchu, Taiwan ; Tseng, C.-L. ; Chen, Y.H. ; Chuang, S.C.
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Based on statistical learning theory, support vector machines (SVM) is a powerful tool for various classification problems, such as pattern recognition and speaker identification etc. However, training SVM consumes large memory and long computing time. This work proposes a cluster-based learning methodology to reduce training time and the memory size for SVM. By using k-means based clustering technique, training data at boundary of each cluster were selected for SVM learning. We also applied this technique to text-independent speaker identification problems. Without deteriorating recognition performance, the training data and time can be reduced up to 75% and 87.5% respectively.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:1 )

Date of Conference:

25-29 July 2004