Speaker verification with feature-space MAPLR parameters

IEEE Transactions on Audio, Speech and Language Processing

Volumn 19, Issue 3, 2011, Pages 505-515

  Zhu, Donglai ^a   Ma, Bin ^a   Li, Haizhou ^a  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

Feature transform; maximum a posteriori; speaker recognition; support vector machine (SVM)

Indexed keywords

AFFINE TRANSFORM; BIAS VECTORS; FEATURE TRANSFORM; FIRST ORDER; GAUSSIAN MIXTURE MODEL; MAXIMUM A POSTERIORI; MAXIMUM A POSTERIORI ALGORITHM; NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY; NUMERICAL PROBLEMS; REGRESSION FUNCTION; SECOND ORDERS; SPEAKER DEPENDENTS; SPEAKER RECOGNITION; SPEAKER VERIFICATION; SPEAKER VERIFICATION SYSTEM; SUFFICIENT STATISTICS; SUPPORT VECTOR MACHINE (SVM); TRANSFORM MATRICES;

BLIND SOURCE SEPARATION; FEATURE EXTRACTION; MATHEMATICAL TRANSFORMATIONS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; VECTORS;

SPEECH RECOGNITION;

EID: 78649983534     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2010.2051269     Document Type: Article

References (34)

1. D. A. Reynolds, T. F. Quatieri, and B. D. Robert, "Speaker verification using adapted Gaussian mixture models," Digital Signal Process., vol. 10, pp. 19-41, 2000.
2. W. M. Campbell, "Generalized linear discriminant sequence kernels for speaker recognition," in Proc. ICASSP, 2002, pp. 161-164.
3. W. M. Campbell, J. P. Campbell, D. A. Reynolds, D. A. Jones, and T. R. Leek, "Phonetic speaker recognition with support vector machines," in Proc. NIPS, 2003, pp. 1377-1384.
4. W. M. Campbell, D. E. Sturim, D. A. Reynolds, and A. Solomonoff, "SVM based speaker verification using a GMMsupervector kernel and NAP variability compensation," in Proc. ICASSP, 2006, pp. 97-100.
5. A. Stolcke, S. S. Kajarekar, L. Ferrer, and E. Shrinberg, "Speaker recognition with session variability normalization based on MLLR adaptation transforms," IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 7, pp. 1987-1998, Sep. 2007.
6. J.-L. Gauvain and C.-H. Lee, "Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains," IEEE Trans. Speech Audio Process., vol. 2, no. 2, pp. 291-298, Apr. 1994.
7. C. J. Leggetter and P. C. Woodland, "Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models," Comput.Speech Lang., vol. 9, pp. 171-185, 1995.
8. M. J. F. Gales and P. C. Woodland, "Mean and variance adaptation within the MLLR framework," Comput. Speech Lang., vol. 10, pp. 249-264, 1996.
9. Z. N. Karam and W. M. Campbell, "A new kernel for SVM MLLR based speaker recognition," in Proc. Interspeech, 2007, pp. 290-293.
10. M. Ferrás, C. C. Leung, C. Barras, and J.-L. Gauvain, "MLLR techniques for speaker recognition," in Proc. IEEE Odyssey'08: Speaker Lang.Workshop, 2008.
11. C. J. Leggetter and P. C.Woodland, "Flexible speaker adaptation using maximum likelihood linear regression," in Proc. ARPA SLS Technol. Workshop, 1995, pp. 110-115.
12. W. Chou, "Maximum posterior linear regression with elliptically symmetric matrix variate priors," in Proc. Eurospeech, 1999, pp. 1-4.
13. O. Siohan, C. Chesta, and C.-H. Lee, "Hidden Markov model adaptation using maximum a posteriori linear regression," in Proc. Workshop Robust Methods for Speech Recognition in Adverse Conditions, 1999, pp. 147-150.
14. X. Lei, J. Hamaker, and X. He, "Robust feature space adaptation for telephony speech recognition," in Proc. ICSLP, 2006, pp. 773-776.
15. M. J. F. Gales, "Maximum likelihood linear transformations for HMMbased speech recognition," Comput. Speech Lang., vol. 12, pp. 75-98, 1998.
16. M. Rahim and B.-H. Juang, "Signal bias removal by maximum likelihood estimation for robust telephone speech recognition," IEEE Trans. Speech Audio Process., vol. 4, no. 1, pp. 19-30, Jan. 1996.
17. A. Sankar and C.-H. Lee, "A maximum likelihood approach to stochastic matching for robust speech recognition," IEEE Trans. Speech Audio Process., vol. 4, no. 3, pp. 190-202, May 1996.
18. Q. Huo and D. Zhu, "Robust speech recognition based on structured modeling, irrelevant variability normalization and unsupervised online adaptation," in Proc. ICASSP, 2009, pp. 4637-4640.
19. D. Zhu, B. Ma, and H. Li, "Using MAP estimation of feature transformation for speaker recognition," in Proc. Interspeech, 2008, pp. 849-852.
20. D. Zhu, B. Ma, and H. Li, "Joint MAP adaptation of feature transformation and Gaussian mixture model for speaker recognition," in Proc. ICASSP, 2009, pp. 4045-4048.
21. T. Anastasakos, J. McDonough, R. Schwartz, and J. Makhoul, "A compact model for speaker adaptive training," in Proc. ICSLP, 1996, pp. 1137-1140.
22. V. V. Digalakis, D. Rtischev, and L. G. Neumeyer, "Speaker adaptation using constrained estimation of Gaussian mixtures," IEEE Trans. Speech Audio Process., vol. 3, no. 5, pp. 357-366, Sep. 1995.
23. D. A. Reynolds, T. F. Quatieri, and B. D. Robert, "Speaker verification using adapted Gaussian mixture models," Digital Signal Process., vol. 10, pp. 19-41, 2000.
24. P. Kenny, P. Ouellet, N. Dehak, V. Gupta, and P. Dumouchel, "A study of interspeaker variability in speaker verification," IEEE Trans. Audio, Speech, Lang. Process., vol. 16, no. 5, pp. 980-988, Jul. 2008.
25. A. Solomonoff, W. M. Campbell, and I. Boardman, "Advances in channel compensation for SVM speaker recognition," in Proc. ICASSP, 2005, pp. 629-632.
26. A. O. Hatch, S. Kajarekar, and A. Stolcke, "Within-class covariance normalization for SVM-Based speaker recognition," in Proc. Interspeech, 2006, pp. 1471-1474.
27. C. Vair, D. Colibro, F. Castaldo, E. Dalmasso, and P. Laface, "Channel factors compensation in model and feature domain for speaker recognition," in Proc. IEEE Odyssey'06: Speaker Lang. Workshop, 2006.
28. R. Auckenthaler, M. Carey, and H. Lloyd-Thomas, "Score normalization for text-independent speaker verification systems," Digital Signal Process., vol. 10, pp. 42-54, 2000.
29. R. C. Rose, E. M. Hofstetter, and D. A. Reynolds, "Integrated models of signal and background with application to speaker identification in noise," IEEE Trans. Speech Audio Process., vol. 2, no. 2, pp. 245-257, Apr. 1994.
30. M. Afify, Y. Gong, and J.-P. Haton, "A general joint additive and convolutive bias compensation approach applied to noisy Lombard speech recognition," IEEE Trans. Speech Audio Process., vol. 6, no. 6, pp. 524-538, Nov. 1998.
31. J. Wu and Q. Huo, "A switching linear Gaussian hidden Markov model and its application to nonstationary noise compensation for robust speech recognition," in Proc. ICASSP, 2003, pp. 977-980.
32. R. Collobert and S. Bengio, "SVMTorch: Support vector machines for large-scale regression problems," J. Mach. Learn. Res., vol. 1, pp. 143-160, 2001.
33. NIST Speaker Recognition Evaluation, [Online]. Available: http://www.itl.nist.gov/iad/mig/tests/sre
34. Z. N. Karam and W. M. Campbell, "A multi-class MLLR kernel for SVM speaker recognition," in Proc. ICASSP, 2008, pp. 4117-4120.