A study of various composite kernels for kernel eigenvoice speaker adaptation

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 1, Issue , 2004, Pages

  Mak, Brian ^a   Kwok, James T ^a   Ho, Simon ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

EIGENVALUES AND EIGENFUNCTIONS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; SPEECH ANALYSIS; SPEECH PROCESSING;

EIGENVOICE BASED METHODS; MAXIMUM LIKELIHOOD LINEAR REGRESSION (MLLR); SPEAKER DEPENDENT (SD) MODELS;

SPEECH RECOGNITION;

EID: 4544261737     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. J.L. Gauvain and C.H. Lee, "Maximum a Posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains," IEEE Transactions on Speech and Audio Processing, vol. 2, no. 2, pp. 291-298, April 1994.
2. C.J. Leggetter and P.C. Woodland, "Maximum Likelihood Linear Regression for Speaker Adaptation of Continuous Density Hidden Markov Models," Journal of Computer Speech and Language, vol. 9. pp. 171-185, 1995.
3. R. Kuhn, J.-C. Junqua, P. Nguyen, and N. Niedzielski, "Rapid Speaker Adaptation in Eigenvoioe Space," IEEE Transactions on Speech and Audio Processing, vol. 8, no. 4, pp. 695-707, Nov 2000.
4. M. Turk and A. Pentland, "Face recognition using eigenface," in Proceedings of the International Conference on Computer Vision and Pattern Recognition, 1991, pp. 586-591.
5. R. Kuhn, F. Perronnin, P. Nguyen, J. C. Junqua, and L. Rigazio, "Very Fast Adaptation with a Compact Context-Dependent Eigen-voice Model," in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2001.
6. K. T. Chen, W. W. Liau, H. M. Wang, and L. S. Lee, "Fast Speaker Adaptation using Eigenspace-based Maximum Likelihood Linear Regression," in Proceedings of the International Conference on Spoken Language Processing, 2000, vol. 3, pp. 742-745.
7. B. Zhou and J. Hansen, "A Novel Algorithm for Rapid Speaker Adaptation Based on Structural Maximum Likelihood Eigenspace Mapping," in Proceedings of the European Conference on Speech Communication and Technology, Aalborg, Denmark, Sept 2001, vol. 2, pp. 1215-1218.
8. B. Schölkopf, A. Smola, and K.R. Müller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Computation, vol. 10. pp. 1299-1319, 1998.
9. J. T. Kwok, B. Mak, and S. Ho, "Eigenvoice Speaker Adaptation via Composite Kernel PCA," in Advances in Neural Information Processing Systems, Vancouver, Canada, Dec 2003.
10. R. G. Leonard, "A Database for Speaker-Independent Digit Recognition," in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 1984, vol. 3, pp. 4211-4214.
11. A.P. Dempster, N.M. Laird, and D.B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society: Series B, vol. 39, no. 1, pp. 1-38, 1977.
12. B. Schölkopf and A.J. Smola, Learning with Kernels, MIT, 2002.
13. A.J. Smola, O.L. Mangasarian, and B. Schölkopf, "Sparse kernel feature analysis," Tech. Rep. 99-03, Data Mining Institute, University of Wisconsin, Madison, 1999.
14. M.G. Genton, "Classes of kernels for machine learning: A statistics perspective," Journal of Machine Learning Research, vol. 2, pp. 299-312, 2001.
15. J. T. Kwok and I. W. Tsang, "The Pre-Image Problem in Kernel Methods," in Proceedings of the 20th International Conference on Machine Learning, Washington, D.C., USA, August 2003, pp. 408-415.