Reverberation model-based decoding in the logmelspec domain for robust distant-talking speech recognition

IEEE Transactions on Audio, Speech and Language Processing

Volumn 18, Issue 7, 2010, Pages 1676-1691

  Sehr, Armin ^a   Maas, Roland ^a   Kellermann, Walter ^a  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

Author keywords

Acoustic modeling; distant talking automatic speech recognition (ASR); model based dereverberation; reverberation model; robust ASR

Indexed keywords

ACOUSTIC MODEL; ACOUSTIC MODELING; AUTOMATIC SPEECH RECOGNITION; COMBINATION OPERATION; CONNECTED DIGITS; FEATURE DOMAIN; HIGH FLEXIBILITY; IN-DEPTH ANALYSIS; JOINT DENSITIES; MODEL-BASED; MODEL-BASED DEREVERBERATION; NON-LINEAR OPTIMIZATION ALGORITHMS; OPTIMIZATION OPERATION; OPTIMIZATION PROBLEMS; PARAMETERS ESTIMATED; ROBUST ASR; STATISTICAL PROPERTIES;

CONSTRAINED OPTIMIZATION; CONTINUOUS SPEECH RECOGNITION; HIDDEN MARKOV MODELS; NUMERICAL METHODS; VITERBI ALGORITHM;

REVERBERATION;

EID: 77955683144     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2010.2050511     Document Type: Article

References (42)

1. A. Sehr, M. Zeller, and W. Kellermann, "Distant-talking continuous speech recognition based on a novel reverberation model in the feature domain," in Proc. Interspeech, 2006, pp. 769-772.
2. M. Gales and S. Young, "The application of hidden Markov models in speech recognition," Foundat. Trends Signal Process., vol.1, no.3, pp. 195-304, 2007.
3. A. Sehr and W. Kellermann, "Towards robust distant-talking automatic speech recognition in reverberant environments," in Topics in Speech and Audio Processing in Adverse Environments, E. Hänsler and G. Schmidt, Eds. Berlin: Springer, 2008, pp. 679-728.
4. B. E. D. Kingsbury and N. Morgan, "Recognizing reverberant speech with RASTA-PLP," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 1997, pp. 1259-1262.
5. R. Petrick, K. Lohde, M.Wolff, and R. Hoffmann, "The harming part of room acoustics in automatic speech recognition," in Proc. Interspeech, Aug. 2007, pp. 1094-1097.
6. X. Huang, A. Acero, and H.-W. Hon, Spoken Language Processing: A Guide to Theory, Algorithm, and System Development. Upper Saddle River, NJ: Prentice-Hall, 2001.
7. M. Miyoshi and Y. Kaneda, "Inverse filtering of room acoustics," IEEE Trans. Acoust., Speech, Signal Process., vol.36, no.2, pp. 145-152, Feb. 1988.
8. T. Hikichi, M. Delcroix, and M. Miyoshi, "Inverse filtering for speech dereverberation less sensitive to noise and room transfer function fluctuations," EURASIP J. Adv. Signal Process., vol.2007, 2007.
9. T. Nakatani, B.-H. Juang, T. Yoshioka, K. Kinoshita, M. Delcroix, and M. Miyoshi, "Speech dereverberation based on maximum-likelihood estimation with time-varying Gaussian source model," IEEE Trans. Audio, Speech, Lang. Process., vol.16, no.8, pp. 1512-1527, Nov. 2008.
10. H. Buchner and W. Kellermann, "TRINICON for dereverberation of speech and audio signals," in Speech Dereverberation, P. Naylor and N. Gaubitch, Eds. Berlin: Springer.
11. K. Lebart and J. M. Boucher, "A new method based on spectral subtraction for speech dereverberation," Acta Acust., vol.87, pp. 359-366, 2001.
12. E. Habets, S. Gannot, and I. Cohen, "Late reverberant spectral variance estimation based on a statistical model," IEEE Signal Process. Lett., vol.16, pp. 770-773, Sep. 2009.
13. K. Kinoshita, M. Delcroix, T. Nakatani, and M. Miyoshi, "Suppression of late reverberation effect on speech signal using long-term multiplestep linear prediction," IEEE Trans. Audio, Speech, Lang. Process., vol.17, no.4, pp. 534-545, May 2009.
14. B. Yegnanarayana and P. S. Murthy, "Enhancement of reverberant speech using LP residual signal," IEEE Trans. Speech Audio Process., vol.8, pp. 267-281, May 2000.
15. N. D. Gaubitch, P. A. Naylor, and D. B. Ward, "On the use of linear prediction for dereverberation of speech," in Proc. IEEE Int. Workshop Acoust. Echo Noise Control (IWAENC), Sep. 2003, pp. 99-102.
16. H. Hermansky and N. Morgan, "RASTA processing of speech," IEEE Trans. Speech Audio Process., vol.2, pp. 578-589, Oct. 1994.
17. B. Atal, "Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification," J. Acoust. Soc. Amer., vol.55, pp. 1304-1312, 1974.
18. C. Avendano, S. Tibrewala, and H. Hermansky, "Multiresolution channel normalization for ASR in reverberant environments," in Proc. Eurospeech, 1997, pp. 1107-1110.
19. M. Wölfel, "Enhanced speech features by single-channel joint compensation of noise and reverberation," IEEE Trans. Audio, Speech, Lang. Process., vol.17, no.2, pp. 312-323, Feb. 2009.
20. D. Giuliani, M. Matassoni, M. Omologo, and P. Svaizer, "Training of HMM with filtered speech material for hands-free recognition," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP),Mar. 1999, vol.1, pp. 449-452.
21. V. Stahl, A. Fischer, and R. Bippus, "Acoustic synthesis of training data for speech recognition in living-room environments," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP),May 2001, vol.1, pp. 285-288. (Pubitemid 32839243)
22. T. Takiguchi, M. Nishimura, and Y. Ariki, "Acoustic model adaptation using first-order linear prediction for reverberant speech," IEICE Trans. Inf. Syst., vol.E89-D, no.3, pp. 908-914, Mar. 2006.
23. C. K. Raut, T. Nishimoto, and S. Sagayama, "Model adaptation for long convolutional distortion by maximum likelihood based state filtering approach," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), May 2006, pp. I-1133-I-1136.
24. H.-G. Hirsch and H. Finster, "A new HMM adaptation approach for the case of a hands-free speech input in reverberant rooms," in Proc. Interspeech, Sep. 2006, pp. 781-783.
25. J. B. K. J. Palomäki and G. J. Brown, "Techniques for handling convolutional distortion with 'missing data' automatic speech recognition," Speech Commun., vol.43, no.1-2, pp. 123-142, Jun. 2004.
26. M. Delcroix, T. Nakatani, and S. Watanabe, "Static and dynamic variance compensation for recognition of reverberant speech with dereverberation preprocessing," IEEE Trans. Audio, Speech, Lang. Process., vol.17, no.2, pp. 324-334, Feb. 2009.
27. A. Sehr, R. Maas, and W. Kellermann, "Model-based dereverberation in the logmelspec domain for robust distant-talking speech recognition," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 2010, pp. 4298-4301.
28. A. Sehr and W. Kellermann, "New results for feature-domain reverberation modeling," in Proc. JointWorkshop Hands-free Speech Commun. Microphone Arrays (HSCMA), 2008.
29. A. P. Varga and R. K. Moore, "Hidden markov model decomposition of speech and noise," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 1990, pp. 845-848.
30. A. Sehr and W. Kellermann, "A simplified decoding method for a robust distant-talking ASR concept based on feature-domain dereverberation, " in Proc. Int. Workshop Acoust. Echo Noise Control (IWAENC), 2008.
31. R. Fletcher, Practical Methods of Optimization, 2nd ed. Chichester, U.K.: Wiley, 2000.
32. R. Maas, "Evaluierung numerischer Optimierungsverfahren für die robuste Spracherkennung nach dem REMOS-Konzept," M.Sc. thesis, University of Erlangen-Nuremberg, Erlangen, Germany, 2009.
33. A. Sehr, Y. Zheng, E. Nöth, and W. Kellermann, "Maximum likelihood estimation of a reverberation model for robust distant-talking speech recognition," in Proc. Eur. Signal Process. Conf. (EUSIPCO), 2007, pp. 1299-1303.
34. A. Sehr, J. Y. C. Wen, W. Kellermann, and P. A. Naylor, "A combined approach for estimating a feature-domain reverberation model in non-diffuse environments," in Proc. Int. Workshop Acoust. Echo Noise Control (IWAENC), 2008.
35. J. Y. C. Wen, A. Sehr, P. A. Naylor, and W. Kellermann, "Blind estimation of a feature-domain reverberation model in non-diffuse environments with variance adjustment," in Proc. Eur. Signal Process. Conf. (EUSIPCO), 2009, pp. 175-179.
36. J. B. Allen and D. A. Berkley, "Image method for efficiently simulating small-room acoustics," J. Acoust. Soc. Amer., vol.65, no.4, pp. 943-950, Apr. 1979.
37. H. G. Hirsch and D. Pearce, "The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions," in Proc. ISCA ITRW ASR2000 Autom. Speech Recogn.: Challenges Next Millennium, 2000.
38. HTK [Online]. Available: http://htk.eng.cam.ac.uk/
39. A. Wächter and L. Biegler, "On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming," Math. Program., vol.106, no.1, pp. 25-57, 2006.
40. S.Young, G. Evermann, D.Kershaw, G. Moore, J. Odell, D. Ollason, D. Povey, V. Valtchev, and P.Woodland, The HTK Book (for HTK Version 3.2). Cambridge, U.K.: Cambridge Univ. Eng. Dept., 2002.
41. "Sound scene database in real acoustical environments," Real World Computing Partnership, 2001.
42. R. G. Leonard, "A database for speaker-independent digit recognition," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 1984, pp. 42.11.1-42.11.4.