Local fuzzy PCA based GMM with dimension reduction on speaker identification

Authors:
Ki Yong Lee
Affiliations:
School of Electronic Engineering, Soong Sil University, Seoul 156743, Korea
Venue:
Pattern Recognition Letters
Year:
2004

Citing 6
Cited 1

Unsupervised Optimal Fuzzy Clustering

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Dimension reduction by local principal component analysis

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Mixtures of probabilistic principal component analyzers

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Joint estimation of feature transformation parameters and Gaussian mixture model for speaker identification

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On the use of orthogonal GMM in speaker recognition

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A call-independent and automatic acoustic system for the individual recognition of animals: A novel model using four passerines

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Abstract

To reduce the high dimensionality required for training of feature vectors in speaker identification, we propose an efficient GMM based on local PCA with fuzzy clustering. The proposed method firstly partitions the data space into several disjoint clusters by fuzzy clustering, and then performs PCA using the fuzzy covariance matrix on each cluster. Finally, the GMM for speaker is obtained from the transformed feature vectors with reduced dimension in each cluster. Compared to the conventional GMM with diagonal covariance matrix, the proposed method shows faster result with less storage maintaining same performance.