Robust speech recognition using model-based feature enhancement

EUROSPEECH 2003 - 8th European Conference on Speech Communication and Technology

Volumn , Issue , 2003, Pages 17-20

  Stouten, Veronique ^a,b   Van Hamme, Hugo ^a   Demuynck, Kris ^a   Wambacq, Patrick ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b RESEARCH FOUNDATION FLANDERS FWO   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ADDITIVE NOISE; FEATURE EXTRACTION; SPEECH; SPEECH COMMUNICATION;

AUTOMATIC SPEECH RECOGNITION SYSTEM; BACKGROUND NOISE; CEPSTRAL FEATURES; FEATURE ENHANCEMENT; FEATURE ENHANCEMENT TECHNIQUES; NOISE ROBUSTNESS; ROBUST SPEECH RECOGNITION; VECTOR TAYLOR SERIES;

SPEECH RECOGNITION;

EID: 85009228863     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Y. Gong, "Speech recognition in noisy environments : A survey," Speech Comm., vol. 16, no. 3, pp. 261-291, 1995.
2. M.J.F. Gales and S.J. Young, "Robust continuous speech recognition using Parallel Model Combination," IEEE Trans. on SAP, vol. 4, no. 5, pp. 352-359, 1996.
3. A.P. Varga and R.K. Moore, "Hidden Markov model decomposition of speech and noise," in Proc. ICASSP, Albuquerque, U.S.A., Apr. 1990, pp. 845-848.
4. J. Droppo, L. Deng, and A. Acero, "Evaluation of the SPLICE algorithm on the Aurora2 database," in Proc. EUROSPEECH, Aalborg, Denmark, Sept. 2001, pp. 217- 220.
5. M.P. Cooke, P.D. Green, L. Josifovski, and A. Vizinho, "Robust automatic speech recognition with missing and unreliable acoustic data," Speech Comm., vol. 34, no. 3, pp. 267-285, 2001.
6. C. Couvreur and H. Van hamme, "Model-based feature enhancement for noisy speech recognition," in Proc. ICASSP, Istanbul, June 2000, vol. 3, pp. 1719-1722.
7. P. Lockwood and J. Boudy, "Experiments with a nonlinear spectral subtractor (NSS), hidden Markov models and the projection, for robust speech recognition in cars," Speech Comm., vol. 11, no. 2-3, pp. 215-228, 1992.
8. A. Acero, Acoustical and Environmental Robustness in Automatic Speech Recognition, Kluwer Academic Publishers, 1993.
9. M.F.J. Gales, Model-Based Techniques for Noise Robust Speech Recognition, Ph.D. thesis, University of Cambridge, Sept. 1995.
10. P.J. Moreno, B. Raj, and R.M.J. Stern, "A Vector Taylor Series approach for environment-independent speech recognition," in Proc. ICASSP, Atlanta, U.S.A., May 1996, pp. 733-736.
11. H. Sameti, H. Sheikhzadeh, L. Deng, and R. Brennan, "HMM-based strategies for enhancement of speech signals embedded in non-stationary noise," IEEE Trans. on SAP, vol. 6, no. 5, pp. 445-455, 1998.
12. D. Macho, L. Mauuary, B. Noé, 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 Proc. ICSLP, Denver, Colorado, U.S.A., Sept. 2002, pp. 17-20.
13. M. Ida and S. Nakamura, "HMMcomposition-based rapid model adaptation using a priori noise GMM adaptation evaluation on Aurora2 corpus," in Proc. ICSLP, Denver, Colorado, U.S.A., Sept. 2002, pp. 437-440.