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KMID : 0917520070140010163
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Journal of Speech Sciences
2007 Volume.14 No. 1 p.163 ~ p.174
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Modified GMM Training for Inexact Observation and Its Application to Speaker Identification
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Kim Jin-Young
Min So-Hee
Na Seung-You
Choi Hong-Sub
Choi Seung-Ho
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Abstract
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All observation has uncertainty due to noise or channel characteristics. This uncertainty should be counted in the modeling of observation. In this paper we propose a modified optimization object function of a GMM training considering inexact observation. The object function is modified by introducing the concept of observation confidence as a weighting factor of probabilities. The optimization of the proposed criterion is solved using a common EM algorithm. To verify the proposed method we apply it to the speaker recognition domain. The experimental results of text-independent speaker identification with VidTimit DB show that the error rate is reduced from 14.8% to 11.7% by the modified GMM training.
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KEYWORD
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GMM, speaker identification, optimization
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