A NEW STUDY OF GMM-SVM SYSTEM FOR TEXT-DEPENDENT SPEAKER RECOGNITION

This paper presents a new approach and the study of GMM-SVM system for text-dependent speaker recognition on scenario of the fixed pass-phrases. The uniform-split content-based GMM-SVM system is proposed and applied to text-dependent speaker evaluation. We conducted detailed study of the proposed method compared to the baseline GMM-SVM system on the RSR2015 database, which has been designed and collected for the evaluation of text-dependent speaker verification system. The experiment results show that the new approach can significantly reduce the detection error of the target-wrong error type (i.e., target speaker with wrong pass-phrase) while maintaining a low detection error for both imposter-correct and imposter-wrong error types (i.e., imposter with correct pass-phrase and imposter with wrong pass-phrase). We also show that score normalization could be applied with respect to the imposter-wrong distribution as opposed to the imposter-correct distribution.