On the use of PCA in GMM and AR-vector models for text independent speaker verification
Charles B. de Lima, Abraham Alcaim, José A. Apolinário · 2003
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%.