Efficient speaker identification based on robust VQ-PCA

Authors:
Younjeong Lee;Joohun Lee;Ki Yong Lee
Affiliations:
School of Electronic Engineering, Soongsil University, Dongjak-gu, Seoul, Korea;Department of Internet Broadcasting, Dong-Ah Broadcasting College, Samjuk-myeon, Anseong, Gyeonggi-do, Korea;School of Electronic Engineering, Soongsil University, Dongjak-gu, Seoul, Korea and Biometric Engineering Research Center
Venue:
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII
Year:
2003

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Abstract

In this paper, and efficient speaker identification based on robust vector quantization principal component analysis (VQ-PCA) is proposed to solve the problems from outliers and high dimensionelity of training feature vectors in speaker identification. Firstly, the proposed method partitions the data space into several disjoint regions by roust VQ based on M-estimation. Secondly, the robust PCA is obtained from the covariance matrix in each region. Finally, our method obtains the Gaussian Mixture model (GMM) for speaker from the transformed feature vectors with reduced dimension by the robust PCA in each region. Compared to the conventional GMM with diagonal covariance matrix, under the same performance, the proposed method gives faster results with less storage and, moreover, shows robust performance to outliers.