Comparison between factor analysis and GMM support vector machines for speaker verification

We present a comparison between speaker verification systems based on factor analysis modeling and support vector machines using GMM supervectors as features. All systems used the same acoustic features and they were trained and tested on the same data sets. We test two types of kernel (one linear, the other non-linear) for the GMM support vector machines. The results show that factor analysis using speaker factors gives the best results on the core condition of the NIST 2006 speaker recognition evaluation. The difference is particularly marked on the English language subset. Fusion of all systems gave an equal error rate of 4.2% (all trials) and 3.2% (English trials only),

[1]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[2]  Patrick Kenny,et al.  The role of speaker factors in the NIST extended data task , 2008, Odyssey.

[3]  Gérard Chollet,et al.  Support Vector Gmms for Speaker Verification , 2006, 2006 IEEE Odyssey - The Speaker and Language Recognition Workshop.

[4]  Sridha Sridharan,et al.  Feature warping for robust speaker verification , 2001, Odyssey.

[5]  Patrick Kenny,et al.  Linear and non linear kernel GMM supervector machines for speaker verification , 2007, INTERSPEECH.

[6]  Frédéric Bimbot,et al.  D-MAP: a distance-normalized MAP estimation of speaker models for automatic speaker verification , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[7]  William M. Campbell,et al.  Advances in channel compensation for SVM speaker recognition , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

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

[9]  David A. van Leeuwen,et al.  Fusion of Heterogeneous Speaker Recognition Systems in the STBU Submission for the NIST Speaker Recognition Evaluation 2006 , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Douglas E. Sturim,et al.  SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

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

[12]  M. Do.  Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models , 2003, IEEE Signal Processing Letters.