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KMID : 0917520050120020171
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Journal of Speech Sciences
2005 Volume.12 No. 2 p.171 ~ p.182
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Speaker Identification Using GMM Based on LPCA
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Seo Chang-Woo
Lee Youn-Jeong
Lee Ki-Yong
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Abstract
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An efficient GMM (Gaussian mixture modeling) method based on LPCA (local principal component analysis) with VQ (vector quantization) for speaker identification is proposed. To reduce the dimension and correlation of the feature vector, this paper proposes a speaker identification method based on principal component analysis. The proposed method firstly partitions the data space into several disjoint regions by VQ, and then performs PCA in each region. Finally, the GMM for the speaker is obtained from the transformed feature vectors in each region. Compared to the conventional GMM method with diagonal covariance matrix, the proposed method requires less storage and complexity while maintaining the same performance requires less storage and shows faster results.
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KEYWORD
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Speaker Identification, PCA, VQ, GMM
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