Normalization of the speech modulation spectra for robust speech recognition

IEEE Transactions on Audio, Speech and Language Processing

Volumn 16, Issue 8, 2008, Pages 1662-1674

  Xiao, Xiong ^a   Chng, Eng Siong ^a   Li, Haizhou ^a,b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

Aurora task; Feature normalization; Modulation spectrum; Robust speech recognition; Temporal filter

Indexed keywords

AURORA TASK; FEATURE NORMALIZATION; MODULATION SPECTRUM; ROBUST SPEECH RECOGNITION; TEMPORAL FILTER;

MODULATION; POWER SPECTRAL DENSITY; PROBABILITY DISTRIBUTIONS;

SPEECH RECOGNITION;

EID: 70350016998     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2008.2002082     Document Type: Article

References (62)

1. Y. Gong, "Speech recognition in noisy environments: A survey, " Speech Commun., vol. 16, no. 3, pp. 261-291, 1995.
2. L. Deng and X. Huang, "Challenges in adopting speech recognition, " Commun. ACM, vol. 47, no. 1, pp. 69-75, 2004.
3. J. S. Lim and A. V. Oppenheim, "Enhancement and bandwidth compression of noisy speech, " Proc. IEEE, vol. 67, no. 12, pp. 1586-1604, Dec. 1979.
4. S. F. Boll, "Suppression of acoustic noise in speech using spectral subtraction, " IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-27, no. 2, pp. 113-120, Apr. 1979.
5. P. Lockwood and J. Boudy, "Experiments with a nonlinear spectral subtractor (NSS), Hidden Markov models and projection, for robust recognition in cars, " Speech Commun., vol. 11, no. 2-3, pp. 215-228, 1992.
6. Y. Ephraim and D. Malah, "Speech enhancement using a minimum mean square error short time spectral amplitude estimator, " IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-32, no. 6, pp. 1109-1121, Dec. 1984.
7. B. Raj, M. L. Seltzer, and R. M. Stern, "Reconstruction of missing features for robust speech recognition, " Speech Commun., vol. 43, no. 4, pp. 275-296, 2004.
8. M. Afify, "Accurate compensation in the log-spectral domain for noisy speech recognition, " IEEE Trans. Speech Audio Process., vol. 13, no. 3, pp. 388-398, May 2005.
9. 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. 2, pp. 133-143, Mar. 2004.
10. L. Deng, J. Droppo, and A. Acero, "Estimating cepstrum of speech under the presence of noise using a joint prior of static and dynamic features, " IEEE Trans. Speech Audio Process., vol. 12, no. 3, pp. 218-223, May 2004.
11. M. J. F. Gales and S. J. Young, "Cepstral parameter compensation for HMM recognition, " Speech Commun., vol. 12, no. 3, pp. 231-239, Jul. 1993.
12. J. L. Gauvain and C. H. Lee, "Maximum a posterirori estimation for multivariate Gaussian mixture observations of Markov chains, " IEEE Trans. Speech Audio Process., vol. 12, no. 2, pp. 291-298, Apr. 1994.
13. C. J. Leggetter and P. C. Woodland, "Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models, " Comput. Speech Lang., vol. 9, no. 2, pp. 171-185, Apr. 1995.
14. S. Furui, "Cepstral analysis technique for automatic speaker verification, " IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-29, no. 2, pp. 254-272, Apr. 1981.
15. O. Viikki and K. Laurila, "Cepstral domain segmental feature vector normalization for noise robust speech recognition, " Speech Commun., vol. 25, pp. 133-147, 1998.
16. F. Hilger and H. Ney, "Quantile based histogram equalization for noise robust large vocabulary speech recognition, " IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 3, pp. 845-854, May 2006.
17. A. de la Torre, A. M. Peinado, J. C. Segura, J. L. Perez-Cordoba, M. C. Benitez, and A. J. Rubio, "Histogram equalization of speech representation for robust speech recognition, " IEEE Trans. Speech Audio Process., vol. 13, no. 3, pp. 355-366, May 2005.
18. Y. Suh, M. Ji, and H. Kim, "Probabilistic class histogram equalization for robust speech recognition, " IEEE Signal Process. Lett., vol. 14, no. 4, pp. 287-290, Apr. 2007.
19. J. C. Segura, C. Benítez, A. de la Torre, A. J. Rubio, and J. Ramírez, "Cepstral domain segmental nonlinear feature transformations for robust speech recognition, " IEEE Signal Process. Lett., vol. 11, no. 5, pp. 517-520, May 2004.
20. L. E. Baum and T. Petrie, "Statistical inference for probabilistic functions of finite state Markov chains, " Ann. Math. Stat., vol. 37, pp. 1554-1563, 1966.
21. J. K. Baker, "The dragon system-An overview, " IEEE Trans. Acoust., Speech, Signal Process., vol. 23, no. ASSP-1, pp. 24-29, Feb. 1975.
22. F. Jelinek, "Continuous speech recognition by statistical methods, " Proc. IEEE, vol. 64, no. 4, pp. 532-536, Apr. 1976.
23. L. R. Rabiner, "A tutorial on HMM and selected applications in speech recognition, " Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989.
24. M. Ostendorf, V. V. Digalakis, and O. A. Kimball, "From HMM's to segment models: A unified view of stochastic modeling for speech recognition, " IEEE Trans. Speech Audio Process., vol. 4, no. 5, pp. 360-378, Sep. 1996.
25. N. Morgan, Q. Zhu, A. Stolcke, K. Sonmez, S. Sivadas, T. Shinozaki, M. Ostendorf, P. Jain, H. Hermansky, D. Ellis, G. Doddington, B. Chen, O. Cetin, H. Bourlard, and M. Athineos, "Pushing the envelop-aside, " IEEE Signal Process. Mag., vol. 22, no. 5, pp. 81-88, Sep. 2005.
26. B. Kinsgbury, N. Morgan, and S. Greenberg, "Robust speech recognition using the modulation spectrogram, " Speech Commun., vol. 25, pp. 117-132, 1998.
27. S. Furui, "Speaker independent isolated word recognizer using dynamic features of speech spectrum, " IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-34, no. 1, pp. 52-59, Feb. 1986.
28. H. Hermansky and N. Morgan, "RASTA processing of speech, " IEEE Trans. Speech Audio Process., vol. 2, no. 4, pp. 578-589, Oct. 1994.
29. C.-P. Chen and J. A. Bilmes, "MVA processing of speech features, " IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 1, pp. 257-270, Jan. 2007.
30. C. Avendano, S. van Vuuren, and H. Hermansky, "Data based filter design for RASTA-like channel normalization in ASR, " in Proc. ICSLP'96, Philadelphia, PA, Oct. 1996.
31. S. van Vuuren and H. Hermansky, "Data-driven design of RASTA-like filters, " in Proc. Eurospeech'97, Rhodes, Greece, Sep. 1997.
32. H. Hermansky, "The modulation spectrum in the automatic recognition of speech, " in Proc. ASRU'97, Santa Barbara, CA, Dec. 1997, pp. 140-147.
33. M. Shire and B. Chen, "On data-derived temporal processing in speech feature extraction, " in Proc. ICSLP'00, Beijing, China, Oct. 2000.
34. M. L. Shire, "Data-driven modulation filter design under adverse acoustic conditions and using phonetic and syllabic units, " in Proc. Eurospeech'99, Budapest, Hungary, Sep. 1999.
35. J.-W. Hung and L.-S. Lee, "Optimization of temporal filters for constructing robust features in speech recognition, " IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 3, pp. 808-832, May 2006.
36. T. Houtgast and H. J. M. Steeneken, "The modulation transfer function in room acoustics as a predictor of speech intelligibility, " Acustica, vol. 28, pp. 66-73, 1973.
37. T. Houtgast and H. J. M. Steeneken, "A review of the MTF concept in room acoustics and its use for estimating speech intelligibility in auditoria, " J. Acoust. Soc. Amer., vol. 77, no. 3, pp. 1069-1077, 1985.
38. L. Atlas and S. A. Shamma, "Joint acoustic and modulation frequency, " EURASIP J. Appl. Signal Process., vol. 2003, no. 7, pp. 668-675, 2003.
39. R. Drullman, J. M. Festen, and R. Plomp, "Effect of temporal envelope smearing on speech reception, " J. Acoust. Soc. Amer., vol. 95, no. 2, pp. 1053-1064, 1994. (Pubitemid 24056370)
40. R. Drullman, J. M. Festen, and R. Plomp, "Effect of reducing slow temporal modulations on speech reception, " J. Acoust. Soc. Amer., vol. 95, no. 5, pp. 2670-2680, 1994. (Pubitemid 24152861)
41. N. Kanedera, T. Arai, H. Hermansky, and M. Pavel, "On the relative importance of various components of the modulation spectrum for automatic speech recognition, " Speech Commun., vol. 28, no. 1, pp. 43-55, 1999.
42. T. Arai, M. Pavel, H. Hermansky, and C. Avendano, "Syllable intelligibility for temporally filtered LPC cepstral trajectories, " J. Acoust. Soc. Amer., vol. 105, no. 5, pp. 2783-2791, 1999.
43. X. Xiao, E. S. Chng, and H. Li, "Temporal structure normalization of speech feature for robust speech recognition, " IEEE Signal Process. Lett., vol. 14, no. 7, pp. 500-503, Jul. 2007.
44. X. Xiao, E. S. Chng, and H. Li, "Normalizing the speech modulation spectrum for robust speech recognition, " in Proc. ICASSP'07, Honolulu, HI, May 2007, pp. 1021-1024.
45. X. Xiao, E. S. Chng, and H. Li, "Evaluating the temporal structure normalisation technique on the Aurora-4 task, " in Proc. Eurospeech'07, Antwerp, Belgium, Sep. 2007, pp. 1070-1073.
46. H. Hermansky, "Perceptual linear predictive (PLP) analysis of speech, " J. Acoust. Soc. Amer., vol. 87, no. 4, pp. 1738-1752, 1990.
47. S. L. Marple, Digital Spectral Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1987.
48. S. S. Haykin, Communication Systems. New York: Wiley, 1983.
49. H. Dudley, "Remaking speech, " J. Acoust. Soc. Amer., vol. 11, no. 2, pp. 169-177, 1939.
50. D. Pearce and H.-G. Hirsch, "The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions, " in Proc. ICSLP'00, Beijing, China, Oct. 2000, vol. 4, pp. 29-32.
51. C. Nadeu, P. Pachès-Leal, and B.-H. Juang, "Filtering the time sequences of spectral parameters for speech recognition, " Speech Commun., vol. 22, no. 4, pp. 315-332, Sep. 1997.
52. A. Kusumoto, T. Arai, K. Kinoshita, N. Hodoshima, and N. Vaughan, "Modulation enhancement of speech by a pre-processing algorithm for improving intelligibility in reverberant environments, " Speech Commun., vol. 45, pp. 101-113, 2005. (Pubitemid 40220188)
53. T. Schultz and K. Kirchhoff, Multilingual Speech Processing. Norwell, MA: Academic, 2006.
54. S. Davis and P. Mermelstein, "Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences, " IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-28, pp. 357-366, Aug. 1980.
55. N. Merhav and C.-H. Lee, "On the asymptotic statistical behavior of empirical cepstral coefficients, " IEEE Trans. Signal Process., vol. 41, no. 5, pp. 1990-1993, May 1993.
56. L. García, J. Segura, C. Benítez, J. Ramírez, and A. de la Torre, "Normalization of the inter-frame information using smoothing filtering, " in Proc. Interspeech'06, Pittsburgh, PA, Sep. 2006, pp. 369-372.
57. S. V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction, 2nd ed. New York: Wiley, 2000.
58. F. D. de Maria, J. Vicente-Pena, A. Gallardo-Antolin, and C. Pelaez-Moreno, "Linear equalization of the modulation spectra: A novel approach for noisy speech recognition, " in Proc. ICASSP'03, Hong Kong, China, Apr. 2003, pp. II-141-II-144.
59. N. Parihar and J. Picone, "Aurora Working Group: DSR Front End LVCSR Evaluation AU/384/02, " Inst. for Signal and Infomation Process., Mississippi State Univ., Mississippi, MS, Dec. 2002, Tech. Rep.
60. E. L. Lehmann, Testing Statistical Hypothesis, 2nd ed. New York: Wiley, 1986.
61. D. P. W. Ellis, "PLP and RASTA (and MFCC, and Inversion) in Matlab, " 2005 [Online]. Available: http://www.ee.columbia.edu/dpwe/resources/ matlab/rastamat/.
62. S. Young et al., The HTK Book, 3rd ed. Cambridge, U.K.: Cambridge Univ. Press, 2006.