On the use of PCA in GMM and AR-vector models for text independent speaker verification

This paper examines the role of the principal components analysis (PCA) on the performance of two classification systems for text independent speaker verification: the Gaussian mixture model (GMM) and the AR-vector model. The use of the PCA transform resulted in an improvement in the performance of...

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Bibliographic Details
Main Authors: de Lima, C.B., Alcaim, A., Apolinario, J.A.
Format: Conference Proceeding
Language:English
Subjects:
Covariance matrix
Error analysis
Hidden Markov models
Loudspeakers
Principal component analysis
Speaker recognition
Speech
Telephony
Testing
Training data
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Summary:This paper examines the role of the principal components analysis (PCA) on the performance of two classification systems for text independent speaker verification: the Gaussian mixture model (GMM) and the AR-vector model. The use of the PCA transform resulted in an improvement in the performance of the GMM for training times of 60 s and 30 s. However, the advantage of using PCA was not observed for the AR-vector model. For the case of 10 s training time, there was no benefit in using PCA even with GMM. In this situation, the AR-vector is superior for a 10 s test and worse for a 3 s test. In this latter case, however, all systems yielded error rates above 7%.
DOI:10.1109/ICDSP.2002.1028160