Improvements in Factor Analysis Based Speaker Verification

We present the results of speaker verification experiments conducted on the NIST 2005 evaluation data using a factor analysis of speaker and session variability in 6 telephone speech corpora distributed by the Linguistic Data Consortium. We demonstrate the effectiveness of zt-norm score normalization and a new decision criterion for speaker recognition which can handle large numbers of t-norm speakers and large numbers of speaker factors at little computational cost. The best result we obtained was a detection cost of 0.016 on the core condition (all trials) of the evaluation

[1]  Patrick Kenny,et al.  Eigenvoice modeling with sparse training data , 2005, IEEE Transactions on Speech and Audio Processing.

[2]  Patrick Kenny,et al.  Speaker and Session Variability in GMM-Based Speaker Verification , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[3]  Sridha Sridharan,et al.  Modelling session variability in text-independent speaker verification , 2005, INTERSPEECH.

[4]  Patrick Kenny,et al.  Joint Factor Analysis Versus Eigenchannels in Speaker Recognition , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[5]  Patrick Kenny,et al.  The Geometry of the Channel Space in GMM-Based Speaker Recognition , 2006, 2006 IEEE Odyssey - The Speaker and Language Recognition Workshop.

[6]  Patrick Kenny,et al.  Joint Factor Analysis of Speaker and Session Variability: Theory and Algorithms , 2006 .

[7]  Patrick Kenny,et al.  Factor analysis simplified [speaker verification applications] , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..