Noise compensation methods for hidden markov model speech recognition in adverse environments

IEEE Transactions on Speech and Audio Processing

Volumn 5, Issue 1, 1997, Pages 11-21

  Vaseghi, Saeed V ^a   Milner, Ben P ^b  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

^b MARTLESHAM HEATH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC SIGNAL PROCESSING; DATABASE SYSTEMS; DIGITAL FILTERS; INTERFERENCE SUPPRESSION; MARKOV PROCESSES; MATHEMATICAL MODELS; MATRIX ALGEBRA; SIGNAL TO NOISE RATIO; VECTORS;

CEPSTRAL TIME MATRIX; NOISE COMPENSATION METHODS; WIENER FILTERS;

SPEECH RECOGNITION;

EID: 0030779363     PISSN: 10636676     EISSN: None     Source Type: Journal    
DOI: 10.1109/89.554264     Document Type: Article

References (30)

1. J. Deller, J. G. Proakis, and H. L. Hansen, Discrete-Time Processing of Speech Signals. New York: Macmillan, 1993.
2. S. V. Vaseghi, Advanced Signal Processing and Digital Noise Reduction. New York: Wiley, 1996.
3. B. H. Juang, Speech recognition in adverse environments, Compiil. Speech La?iguage, pp. 275-294, 1991.
4. S. F. Boll, Suppression of acoustic noise in speech using spectral subtraction, IEEE Trans. Acoiisl., Speech, Signal Processing, vol. ASSP-27, pp. 113-120, Apr. 1979.
5. A. D. Berstein and I. D. Shallom, An hypothesized Wiener filtering approach to noisy speech recognition Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, 1991, pp. 913-916.
6. Y. Ephraim, D. Malah, and B. H. Juang, On the application of hidden Markov models for enhancing noisy speech, IEEE Trans. Acoust., Speech, Signal Processing, vol 37, pp. 1846-1856, Dec. 1989.
7. S. V. Vaseghi and B. P. Milner, Noise adaptive hidden Markov models based on Wiener filters Proc. Eurospeech, 1993, pp. 1023-1026.
8. J. S. Lim and A. V. Oppenheim, All-pole modeling of degraded speech, IEEE Trans. Acoust., Speech, Signal Processing, vol ASSP-26, pp. 197-210, June 1978.
9. P. Lockwood and J. Boudy, Experiments with a nonlinear spectral subtractor (NSS), hidden Markov models and the projection, for robust speech recognition in cars Proc. Eurospeech, 1991, pp. 79-82.
10. J. S. Bridle, K. M. Ponting, M. D. Brown, and A. \V. Borret, A noise compensating spectrum distance measure applied to automatic speech recognition Proc. lust. Acoust., vol. 6, pt. 4, pp. 307-314, 1984.
11. D. B. Roe, Speech recognition with a noise-adapting codebook Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, 1987, pp. 1139-1142.
12. A. Nadas, D. Nahamoo, and M. A. Picheny, Speech recognition using noise-adaptive prototypes, IEEE Trans. Acoust., Speech, Signal Processing, vol. 37, pp. 1495-1503, Oct. 1989.
13. A. Varga and R. K. Moore, Hidden Markov model decomposition of speech and noise Proc. IEEE Int., Conf. Acoitsl., Speech, Signal Processing, 1990, pp. 845-848.
14. A. Varga, R. Moore, J. Bridle, K. Ponting, and M. Russell, Noise compensation algorithms for use with hidden Markov model based speech recognition Proc. IEEE Int. Conf. Acousl., Speech, Signal Processing, 1988, pp. 481-484.
15. M. J. F. Gales and S. J. Young, An improved approach to the hidden Markov model decomposition Proc. IEEE Int. Conf. Acousl., Speech, Signal Processing, 1992, pp. 729-734.
16. _, HMM recognition in noise using parallel model combination Proc. Eurospeech, 1993, pp. 837-840.
17. D. Mansour and B. H. Juang, The short-time modified coherence representation and noisy speech recognition, IEEE Trans. Acoust., Speech, Signal Processing, vol. 37, pp. 795-804, June 1989.
18. Y. Ariki, S. Mizuta, M. Nagata, and T. Sakai, Spoken-word recognition using dynamic features analyzed by two-dimensional cepstrum, Proc. Inst. Elec. Eng., vol 136, pt. I, no. 2, pp. 133-140, Apr. 1989.
19. S. V. Vasegi, P. N. Connor, and B. P. Milner, Speech modeling using cepstral-time feature matrices in hidden Markov models, Proc. Inst. Elec. Eng., vol. 140, no. 5, pp. 317-320, Oct. 1993.
20. O. Ghitza, Auditory nerve representation as a front-end for speech recognition in a noisy environment, Comput. Speech Language, vol. 1, pp. 109-130, 1986.
21. C. Pisoni, Some acoustic-phonetic correlates of speech produced'in noise Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, 1985, pp. 1581-1584.
22. N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Tune Series, with Engineering Applications. Cambridge, MA: MIT Press, 1949.
23. V. L. Beattie and S. J. Young, Noisy speech recognition using hidden Markov model state based filtering Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, 1991, pp. 917-920.
24. S. V. Vaseghi and B. P Milner, Noisy speech recognition based on HMM's,-Wiener filters and re-evaluation of most likely candidates Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, vol. II, 1991, pp. 103-106.
25. J. A. Nolazco Flores and S. J. Young, Adapting a HMM-based recognizer for noisy speech enhanced by spectral subtraction Proc. Eurospeech, 1993, pp. 829-832.
26. S. Vaseghi and P. Rayner, Detection and suppression of impulsive noise in speech communications systems, Proc. Inst. Elec. Eng., Commun. Speech Vision, pp. 38-46, Feb 1990.
27. S. J. Godsill, The restoration of degraded audio signals, Ph.D. Thesis, Cambridge Univ., UK 1993.
28. J. W. Tukey, Exploratory Data Analysis. Reading, MA: Addison Wesley, 1971.
29. S. Furui, Speaker-independent isolated word recognition using dynamic features of speech spectrum, IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-34, no. 1, pp. 52-59, Feb. 1986.
30. A. P. Varga, H. J. M. Steenken, M. Tomlinson, and D. Jones, 'The NOISEX-92 study on the effect of additive noise on automatic speech recogintion, Tech. Rep., DRA Speech Res. Unit, 1992.