Non-linear feature extraction for robust speech recognition in stationary and non-stationary noise

Computer Speech and Language

Volumn 17, Issue 4, 2003, Pages 381-402

  Zhu, Qifeng ^a   Alwan, Abeer ^a  

^a Department of Civil and Environmental Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC NOISE; ALGORITHMS; COMPUTER SIMULATION; DATABASE SYSTEMS; DEMODULATION; FAST FOURIER TRANSFORMS; FEATURE EXTRACTION; MARKOV PROCESSES; SIGNAL TO NOISE RATIO; SPEECH ANALYSIS; SPEECH PROCESSING;

ACOUSTIC FEATURE EXTRACTION; AUTOMATIC SPEECH RECOGNITION; HARMONIC DEMODULATION; NONSTATIONARY NOISE;

SPEECH RECOGNITION;

EID: 0142009990     PISSN: 08852308     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0885-2308(03)00026-3     Document Type: Article

References (36)

1. Boll, S. F., 1979. Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics, Speech and Signal Processing 27, 113-120.
2. Davis, S. B., Mermelstein, P., 1980. Comparison of parametric representations for monosyllabic world recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech and Signal Processing 28, 357-366.
3. Deng, L., Droppo, J., Acero, A. 2002. A Bayesian approach to speech feature enhancement using the dynamic cepstral prior. Proceedings of the International Conference on Acoustics, Speech and Signal Processing 1, 829-832.
4. Ephraim, Y., Trees, H.L., 1995. A signal subspace approach for speech enhancement. IEEE Transactions on Speech and Audio Processing 3, 257-266.
5. Fant, G., 1960. The Acoustic Theory of Speech Production. S'Gravenhage, Mouton.
6. Furui, S., 1981. Cepstral analysis technique for automatic speaker verification. IEEE Transactions on Acoustics, Speech and Signal Processing 29, 254-272.
7. Gales, M.J.F., 2002. Maximum likelihood multiple subspace projections for hidden Markov models. IEEE Transactions on Speech and Audio Processing 10, 37-47.
8. Gales, M.J.F. Young, S.J., 1996. Robust continuous speech recognition using parallel model combination. IEEE Transactions on Speech and Audio Processing 4, 3352-359.
9. Gauvian, J.L., Lee, C.H., 1994. Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. IEEE Transactions on Speech and Audio Processing 2, 291-298.
10. Greenberg, S., Kingsbury, B., 1997. The modulation spectrogram: in pursuit of an invariant representation of speech. Proceedings of the International Conference on Acoustics, Speech and Signal Processing 3, 1647-1650.
11. Haeb-Umbach, R., Ney, H., 1992. Linear discriminant analysis for improved large vocabulary continuous speech recognition. Proceedings of the International Conference on Acoustics, Speech and Signal Processing 1, 13-16.
12. Haykin, S., 1978. Communication Systems. Wiley, New York.
13. Hermansky, H., 1990. Perceptual linear predictive (PLP) analysis of speech. Journal of Acoustical Society of America 87, 1738-1952.
14. Hermansky, H., Morgan, N., 1994. RASTA processing of speech. IEEE Transactions on Speech and Audio Processing 2, 578-589.
15. Hirsch, H.G., Pearce, D., 2000. The AURORA experimental framework for the performance evaluations of speech recognition systems under noisy condition. Automatic speech recognition: challenges for the next millennium.
16. Juang, B., Rabiner, L., Wilpon, J., 1987. On the use of bandpass liftering in speech recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 35, 947-954.
17. Kajita, S., Itakura, F., 1994. Speech analysis and speech recognition using subband-autocorrelation analysis. Journal of the Acoustical Society 15, 329-338.
18. Kanedra, N., Hermansky, H., Arai, T., 1998. On properties of modulation spectrum for robust automatic speech recognition. Proceedings of the International Conference on Acoustics, Speech and Signal Processing 2, 613-616.
19. Kocsor, A., Toth, L., Kuba, A., Kovacs, K., Jelasity, M., Gyimothy, T., Csirik, J., 2000. A comparative study of several feature transformation and learning methods for phoneme classification. International Journal of Speech Technology 3, 263-276.
20. Leggetter, C., Woodland, P., 1994. Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Proceedings of International Conference on Spoken Language Processing 2, 451-454.
21. Lieb, M., Haeb-Umbach, R., 2000. LDA derived cepstral trajectory filters in adverse environmental conditions. Proceedings of the International Conference on Acoustics, Speech and Signal Processing 2, 105-108.
22. Lockwood, P., Boudy, J., 1992. Experiments with a nonlinear spectral subtractor (NSS), hidden Markov models and the projection of robust speech recognition in cars. Speech Communication 11, 215-228.
23. Medan, Y., Yair, E., Chazan, D., 1991. Super resolution pitch determination of speech signals. IEEE Transactions on Signal Processing 39, 40-48.
24. Milner, B., 1996. Inclusion of temporal information into features for speech recognition. Proceedings of International Conference on Spoken Language Processing 1, 256-259.
25. Paliwa, K.K., Lilly, B.T., 1997. Auditory masking based acoustic front-end for robust speech recognition. Proceedings of IEEE TENCON 1, 165-168.
26. Poter, J.E., Boll, S.F., 1984. Optimal estimators for spectral restoration of noisy speech. Proceedings of International Conference on Spoken Language Processing 3, 18A.2.1.
27. Renevey, P., Drygajlo, A., 2000. Statistical estimation of unreliable features for robust speech recognition. Proceedings of the International Conference on Acoustics, Speech and Signal Processing 3, 1731-1734.
28. Saon, G., Padmanabhan, M., Gopinath, R., Chen, S., 2000. Maximum likelihood discriminant feature spaces. Proceedings of the International Conference on Acoustics, Speech and Signal Processing 2, 1129-1132.
29. Sharma, S., Ellis, D., Kajarekar, S., Hermansky, H., 2000. Feature extraction using nonlinear transformation for robust speech recognition on the Aurora database. Proceedings of the International Conference on Acoustics. Speech and Signal Processing 2, 1117-1120.
30. Strope, B., Alwan, A., 1997. A model of dynamic auditory perception and its application to robust word recognition. IEEE Transactions on Speech and Audio Processing 5, 451-464.
31. Strope, B., Alwan, A., 1998. Robust word recognition using threaded spectral peaks. Proceedings of the International Conference on Acoustics, Speech and Signal Processing 2, 625-629.
32. Virag, N., 1999. Single channel speech enhancement based on masking properties of the human auditory system. IEEE Transactions on Speech and Audio Processing 7, 126-137.
33. Zhu, Q., Alwan, A., 2000a. On the use of variable frame rate analysis in speech recognition. Proceedings of the International Conference on Acoustics, Speech and Signal Processing 3, 1783-1786.
34. Zhu, Q., Alwan, A., 2000b. Amplitude demodulation of speech spectra and its application to noise robust speech recognition. Proceedings of International Conference on Spoken Language Processing 1, 341-344.
35. Zhu, Q., Alwan, A., 2002. The effect of additive noise on speech amplitude spectra: a quantitative approach. The IEEE Signal Processing Letters 9 (9), 275-277.
36. Zhu, Q., Iseli, M., Cui, X., Alwan, A., 2001. Noise robust front-end feature extraction for automatic speech recognition. Proceedings Eurospeech 2001 1, 185-188.