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KMID : 0917520040110020237
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Journal of Speech Sciences
2004 Volume.11 No. 2 p.237 ~ p.245
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Estimation of Mixture Numbers of GMM for Speaker Identification
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Lee Youn-Jeong
Lee Ki-Yong
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Abstract
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In general, Gaussian mixture model(GMM) is used to estimate the speaker model for speaker identification. The parameter estimates of the GMM are obtained by using the expectation-maximization (EM) algorithm for the maximum likelihood(ML) estimation. However, if the number of mixtures isn’t defined well in the GMM, those parameters are obtained inappropriately. The problem to find the number of components is significant to estimate the optimal parameter in mixture model. In this paper, to estimate the optimal number of mixtures, we propose the method that starts from the sufficient mixtures, after, the number is reduced by investigating the mutual information between mixtures for GMM. In result, we can estimate the optimal number of mixtures. The effectiveness of the proposed method is shown by the experiment using artificial data. Also, we performed the speaker identification applying the proposed method comparing with other approaches.
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KEYWORD
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Speaker Identification, GMM, Mutual Information, number of mixtures
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