Noise robust speech recognition using feature compensation based on polynomial regression of utterance SNR

IEEE Transactions on Speech and Audio Processing

Volumn 13, Issue 6, 2005, Pages 1161-1172

  Cui, Xiaodong ^a,b   Alwan, Abeer ^a,b,c,d,e  

^a IEEE   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c Eta Kappa Nu ^*  (United States)

^d NEW YORK ACADEMY OF SCIENCES   (United States)

^e ACOUSTICAL SOCIETY OF AMERICA   (United States)

Author keywords

Feature compensation; Noise robust speech recognition; Polynomial regression; Signal to noise ratio (SNR) estimation

Indexed keywords

FEATURE COMPENSATION; NOISE ROBUST SPEECH RECOGNITION; POLYNOMIAL REGRESSION; SIGNAL-TO-NOISE RATIO (SIR) ESTIMATION;

ALGORITHMS; AUTOMATION; NOISE ABATEMENT; POLYNOMIAL APPROXIMATION; REGRESSION ANALYSIS; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO;

SPEECH RECOGNITION;

EID: 27744539597     PISSN: 10636676     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSA.2005.853002     Document Type: Article

References (26)

1. Y. Gong, "Speech recognition in noisy environments: a survey," Speech Commun., vol. 16, pp. 261-291, 1995.
2. S. Boll, "Suppression of acoutic noise in speech using spectral subtraction," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-27, no. 2, pp. 113-120, 1979.
3. B. Atal, "Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification," J. Acoust. Soc. Amer., vol. 55, no. 6, pp. 1304-1312, 1974.
4. A. Acero, Acoustical and Environmental Robustness in Automatic Speech Recognition. Norwell, MA: Kluwer, 1992.
5. L. Deng, J. Droppo, and A. Acero, "Estimating cepstrum of speech under the presentee of noise using a joint prior of static and dynamic features," IEEE Trans. Speech Audio Process., vol. 12, no. 3, pp. 218-233, May 2004.
6. H. Hermansky and N. Morgan, "RASTA processing of speech," IEEE Trans. Speech Audio Process., vol. 2, pp. 578-589, 1994.
7. H. Hermansky, "Perceptual linear prediction (PLP) analysis of speech," J. Acoust. Soc. Amer., vol. 87, no. 4, pp. 1738-1752, 1990.
8. D. Macho, L. Mauuary, B. Noe, Y. Cheng, D. Ealey, D. Jouvet, H. Kelleher, D. Perace, and F. Saadoun, "Evaluation of a noise-robust DSR front-end on aurora databases," in Proc. Int. Conf. Spoken Language Processing, 2002, pp. 17-20.
9. X. Cui, M. Iseli, Q. Zhu, and A. Alwan, "Evaluation of noise robust features on the Aurora databases," in Proc. Int. Conf. Spoken Language Processing, 2002, pp. 481-484.
10. B. Strope and A. Alwan, "A model of dynamic auditory perception and its application to robust word recognition," IEEE Trans. Speech Audio Process., vol. 5, pp. 451-464, 1997.
11. Q. Zhu and A. Alwan, "Amplitude demodulation of speech and its application to noise robust speech recognition," in Proc. Int. Conf. Spoken Language Processing, 2000, pp. 341-344.
12. _, "On the use of variable frame rate analysis in speech recognition," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 2000, pp. 1783-1786.
13. M. Gales and S. Young, "Robust continuous speech recognition using parallel model combination," IEEE Trans. Speech Audio Process., vol. 4, pp. 352-359, 1996.
14. P. Moreno, "Speech Recognition in Noisy Environments," Ph.D. dissertation, Carnegie Mellon Univ., Pittsburgh, PA, 1996.
15. B. Raj, E. Gouvea, P. Moreno, and R. Stern, "Cepstral compensation by polynomial approximation for environment-independent speech recognition," in Proc. Int. Conf. Spoken Language Processing, 1996, pp. 2340-2343.
16. S. Sagayama, Y. Yamaguchi, S. Takahashi, and J. Takahashi, "Jacobian approach to fast acoustic model adaptation," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 1997, pp. 835-838.
17. C. Leggetter and P. Woodland, "Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models," Comput. Speech Lang., vol. 9, pp. 171-185, 1995.
18. Z. Zhang and S. Furui, "Piecewise-linear transformation-based HMM adaptation for noisy speech," Speech Commun., vol. 42, pp. 43-58, 2004.
19. X. Cui and Y. Gong, "Variable parameter Gaussian mixture hidden Markov modeling for speech recognition," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, vol. 1, 2003, pp. 12-15.
20. A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Statist. Soc., vol. 39, no. 1, pp. 1-38, 1977.
21. Y. Gong, "Noise-dependent Gaussian mixture classifers for robust rejection decision," IEEE Trans. Speech Audio Process., vol. 10, no. 2, pp. 57-64, Mar. 2002.
22. L. Deng, J. Droppo, and A. Acero, "Enhancement of log Mel power spectra of speech using a phase-sensitive model of the acoustic environment and sequential estimation of the corrupting noise," IEEE Trans. Speech Audio Process., vol. 12, no. 3, pp. 133-143, May 2004.
23. Q. Zhu and A. Alwan, "The effect of additive noise on speech amplitude spectra: A quantitative analysis," IEEE Signal Process. Lett., vol. 9, no. 9, pp. 275-277, Sep. 2002.
24. R. Martin, "An efficient algorithm to estimate instantanous SNR of speech signals," in Proc. Eur. Conf. Speech Communication Technology, 1993, pp. 1093-1096.
25. H. Hirsch and D. Pearce, "The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions," in Proc. ASR2000 Int. Workshop on Automatic Speech Recognition, 2000, pp. 181-188.
26. The HTK Book (Version 3.1). Cambridge, U.K.: Cambridge Univ. Press, 2001.