An mcmc approach to joint estimation of clean speech and noise for robust speech recognition

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

Volumn , Issue , 2013, Pages 7107-7111

  Mushtaq, Aleem ^a   Lee, Chin Hui ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Gibbs sampling; Markov chain Monte Carlo; Monte Carlo; noise compensation; particle filters; robust speech recognition

Indexed keywords

GIBBS SAMPLING; MARKOV CHAIN MONTE-CARLO; NOISE COMPENSATION; PARTICLE FILTER; ROBUST SPEECH RECOGNITION;

ACOUSTIC NOISE; DISTRIBUTED COMPUTER SYSTEMS; MONTE CARLO METHODS; SIGNAL FILTERING AND PREDICTION; SPEECH; TARGET TRACKING;

SPEECH RECOGNITION;

EID: 84890447563     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6639041     Document Type: Conference Paper

References (20)

1. J.L. Gauvain and C.-H. Lee, "Maximum a posteriori estimation for multivariate gaussian mixture observations of markov chains," Speech and Audio Processing, IEEE Transactions on, vol. 2, no. 2, pp. 291-298, 1994
2. CJ Leggetter and PC Woodland, "Maximum likelihood linear regression for speaker adaptation of continuous density hidden markov models," Computer speech and language, vol. 9, no. 2, pp. 171, 1995
3. A. Acero, L. Deng, T. Kristjansson, and J. Zhang, "Hmm adaptation using vector taylor series for noisy speech recognition," in Sixth International Conference on Spoken Language Processing, 2000
4. H.K. Kim and R.C. Rose, "Cepstrum-domain acoustic feature compensation based on decomposition of speech and noise for asr in noisy environments," Speech and Audio Processing, IEEE Transactions on, vol. 11, no. 5, pp. 435-446, 2003
5. D. Macho, L. Mauuary, B. Noe, Y.M. Cheng, D. Ealey, D. Jouvet, H. Kelleher, D. Pearce, and F. Saadoun, "Evaluation of a noise-robust dsr front-end on aurora databases," in Seventh International Conference on Spoken Language Processing, 2002
6. P. Assmann and Q. Summerfield, "The perception of speech under adverse conditions," Speech processing in the auditory system, pp. 231-308, 2004
7. S. Greenberg and W. Ainsworth, Listening to speech: An auditory perspective, Lawrence Erlbaum, 2006
8. R.G. Brown and P.Y.C. Hwang, "Introduction to random signals and applied kalman filtering. 1997," NY John Wiley and Sons
9. S. Haykin, Adaptive Filter Theory, 2002, Prentice-Hall
10. R. Singh and B. Raj, "Tracking noise via dynamical systems with a continuum of states," in Acoustics, Speech, and Signal Processing, 2003. Proceedings.( ICASSP'03). 2003 IEEE International Conference on. IEEE, 2003, vol. 1, pp. I-396
11. B. Raj, R. Singh, and R. Stern, "On tracking noise with linear dynamical system models," in Acoustics, Speech, and Signal Processing, 2004. Proceedings.( ICASSP'04). IEEE International Conference on. IEEE, 2004, vol. 1, pp. I-965
12. M. Fujimoto and S. Nakamura, "Sequential nonstationary noise tracking using particle filtering with switching dynamical system," in Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on. IEEE, 2006, vol. 1, pp. I-I
13. A. Mushtaq, Y. Tsao, and C.-H. Lee, "A particle filter feature compensation approach to robust speech recognition," in Eleventh Annual Conference of the International Speech Communication Association, 2010
14. A. Mushtaq and C.-H. Lee, "An integrated approach to feature compensation combining particle filters and hidden markov model for robust speech recognition," in Acoustics, Speech and Signal Processing, 2012. ICASSP 2012 Proceedings. 2012 IEEE International Conference on. IEEE, 2012, vol. 1, pp. I-I
15. A. Mushtaq and C.-H. Lee, "Joint tracking of clean speech and noise using hmms and particle filters for robust speech recognition," Asilomar Conference on Signals, Systems and Computers, 2012
16. M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking," Signal Processing, IEEE Transactions on, vol. 50, no. 2, pp. 174-188, 2002
17. P.J. Moreno, B. Raj, and R.M. Stern, "A vector taylor series approach for environment-independent speech recognition," in Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on. IEEE, 1996, vol. 2, pp. 733-736
18. P.D. Hoff, A first course in Bayesian statistical methods, Springer, 2009
19. A.E. Gelfand and A.F.M. Smith, "Sampling-based approaches to calculating marginal densities," Journal of the American statistical association, vol. 85, no. 410, pp. 398-409, 1990
20. S. Chib and E. Greenberg, "Understanding the metropolis-hastings algorithm," The American Statistician, vol. 49, no. 4, pp. 327-335, 1995.