ALGONQUIN - Learning dynamic noise models from noisy speech for robust speech recognition

Advances in Neural Information Processing Systems

Volumn , Issue , 2002, Pages

  Frey, Brendan J ^a   Kristjansson, Transti T ^a   Dong, Li ^b   Acoro, Alex ^b  

^a UNIVERSITY OF TORONTO   (Canada)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; ITERATIVE METHODS; SPEECH;

APPROXIMATE INFERENCE; GENERALIZED EM ALGORITHMS; NONADAPTIVE ALGORITHM; NONSTATIONARY NOISE; PROBABILISTIC INFERENCE; PROBABILITY MODELING; ROBUST SPEECH RECOGNITION; VECTOR TAYLOR SERIES;

SPEECH RECOGNITION;

EID: 84899023315     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. Attias, H., Piatt, J. C., Acero, A., and Deng, L. 2001. Speech denoising and dereverberation using probabilistic models. In Advances in Neural Information Processing Systems 13. MIT Press, Cambridge MA.
2. Boll, S. 1979. Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics, Speech and Signal Processing, 27:114-120.
3. Deng, L., Acero, A., Plumpe, M., and Huang, X. D. 2000. Laxge-vocabulaxy speech recognition under adverse acoustic environments. In Proceedings of the International Conference on Spoken Language Processing, pages 806-809.
4. Frey, B. J., Deng, L., Acero, A., and Kristjansson, T. 2001. ALGONQUIN: Iterating Laplace's method to remove multiple types of acoustic distortion for robust speech recognition. In Proceedings of Eurospeech 2001.
5. Gales, M. J. F. and Young, S. J. 1996. Robust continuous speech recognition using parallel model combination. IEEE Speech and Audio Processing, 4(5):352-359.
6. Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K. 1998. An introduction to vaxiational methods for graphical models. In Jordan, M. I., editor, Learning in Graphical Models. Kluwer Academic Publishers, Norwell MA.
7. Moreno, P. 1996. Speech Recognition in Noisy Environments. Carnegie Mellon University, Pittsburgh PA. Doctoral dissertation.
8. Neal, R. M. and Hinton, G. E. 1998. A view of the EM algorithm that justifies incremental, spaxse, and other vaxiants. In Jordan, M. I., editor, Learning in Graphical Models, pages 355-368. Kluwer Academic Publishers, Norwell MA.
9. Vaxga, A. P. and Moore, R. K. 1990. Hidden Maxkov model decomposition of speech and noise. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing, pages 845-848. IEEE Press.