Speaker Verification Based on Factor Analysis and SVM

In the text-independent speaker recognition system, the mean-supervector of Gaussian Mixture Models (GMM) and Support Vector Machine (SVM) system can outperform the traditional GMM and Universal Background Models (UBM) system, but the session variability is still one of the most important reasons that deteriorate the performance. In this paper, the factor analysis is tailored to solve the session variability problem of GMM mean-supervector. The proposed algorithm can outperform the Nuisance Attribute Projection (NAP) algorithm. Furthermore, the proposed system based on factor analysis is more stable than the system based on NAP. In the NIST 2006 SRE corpus, the Equal Error Rate (EER) of the proposed system can obtain 6.0%.