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KMID : 0917520050120040031
Journal of Speech Sciences
2005 Volume.12 No. 4 p.31 ~ p.42
Performance Enhancement of Speaker Identification System Based on GMM Using the Modified EM Algorithm
Kim Seong-Jong

Chung Ik-Joo
Abstract
Recently, Gaussian Mixture Model (GMM), a special form of CHMM, has been applied to speaker identification and it has proved that performance of GMM is better than CHMM. Therefore, in this paper the speaker models based on GMM and a new GMM using the modified EM algorithm are introduced and evaluated for text-independent speaker identification. Various experiments were performed to evaluate identification performance of two algorithms. As a result of the experiments, the GMM speaker model attained 94.6% identification accuracy using 40 seconds of training data and 32 mixtures and 97.8% accuracy using 80 seconds of training data and 64 mixtures. On the other hand, the new GMM speaker model achieved 95.0% identification accuracy using 40 seconds of training data and 32 mixtures and 98.2% accuracy using 80 seconds of training data and 64 mixtures. It shows that the new GMM speaker identification performance is better than the GMM speaker identification performance.
KEYWORD
GMM, EM, CHMM, Speaker Identification
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