A comparison of session variability compensation techniques for SVM-based speaker recognition

This paper compares two of the leading techniques for session variability compensation in the context of GMM mean supervector SVM classifiers for speaker recognition: inter-session variability modelling and nuisance attribute projection. The former is incorporated in the GMM model training while the latter is employed as a modified SVM kernel. Results on both the NIST 2005 and 2006 corpora demonstrate the effectiveness of both techniques for reducing the effects of session variation. Further, systemand score-level fusion experiments show that the combination of the two methods provides improved performance.