Open Access

Robust In-Car Speech Recognition Based on Nonlinear Multiple Regressions

EURASIP Journal on Advances in Signal Processing20062007:016921

https://doi.org/10.1155/2007/16921

Received: 31 January 2006

Accepted: 29 October 2006

Published: 26 December 2006

Abstract

We address issues for improving handsfree speech recognition performance in different car environments using a single distant microphone. In this paper, we propose a nonlinear multiple-regression-based enhancement method for in-car speech recognition. In order to develop a data-driven in-car recognition system, we develop an effective algorithm for adapting the regression parameters to different driving conditions. We also devise the model compensation scheme by synthesizing the training data using the optimal regression parameters and by selecting the optimal HMM for the test speech. Based on isolated word recognition experiments conducted in 15 real car environments, the proposed adaptive regression approach shows an advantage in average relative word error rate (WER) reductions of 52.5 and 14.8 , compared to original noisy speech and ETSI advanced front end, respectively.

[12345678910111213141516171819202122232425262728293031323334353637]

Authors’ Affiliations

(1)
Graduate School of Information Science, Nagoya University
(2)
Department of Information Engineering, Faculty of Science and Technology, Meijo University

References

  1. Gong Y: Speech recognition in noisy environments: a survey. Speech Communication 1995,16(3):261-291. 10.1016/0167-6393(94)00059-JView ArticleGoogle Scholar
  2. Davis SB, Mermelstein P: Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing 1980,28(4):357-366. 10.1109/TASSP.1980.1163420View ArticleGoogle Scholar
  3. Hermansky H: Perceptual linear predictive (PLP) analysis of speech. The Journal of the Acoustical Society of America 1990,87(4):1738-1752. 10.1121/1.399423View ArticleGoogle Scholar
  4. Hermansky H, Morgan N: RASTA processing of speech. IEEE Transactions on Speech and Audio Processing 1994,2(4):578-589. 10.1109/89.326616View ArticleGoogle Scholar
  5. Gold B, Morgan N: Speech and Audio Signal Processing: Processing and Perception of Speech and Music. John Wiley & Sons, New York, NY, USA; 1999.Google Scholar
  6. Ghitza O: Auditory models and human performance in tasks related to speech coding and speech recognition. IEEE Transactions on Speech and Audio Processing 1994,2(1):115-132. 10.1109/89.260357View ArticleGoogle Scholar
  7. Boll SF: Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics, Speech, and Signal Processing 1979,27(2):113-120. 10.1109/TASSP.1979.1163209View ArticleGoogle Scholar
  8. Huang X, Acero A, Hon H-W: Spoken Language Processing—A Guide to Theory, Algorithm, and System Development. Prentice-Hall, Englewood Cliffs, NJ, USA; 2001.Google Scholar
  9. Acero A: Acoustical and environmental robustness in automatic speech recognition, Ph.D. thesis. Carnegie Mellon University, Pittsburgh, Pa, USA; 1990.Google Scholar
  10. Leggetter CJ, Woodland PC: Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Computer Speech and Language 1995,9(2):171-185. 10.1006/csla.1995.0010View ArticleGoogle Scholar
  11. Sagayama S, Yamaguchi Y, Takahashi S: Jacobian adaptation of noisy speech models. Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding, December 1997, Santa Barbara, Calif, USA 396-403.View ArticleGoogle Scholar
  12. Sarikaya R, Hansen JHL: Improved Jacobian adaptation for fast acoustic model adaptation in noisy speech recognition. Proceedings of the 6th International Conference on Spoken Language Processing (ICSLP '00), October 2000, Beijing, China 702-705.Google Scholar
  13. Sorensen HBD: A cepstral noise reduction multi-layer neural network. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '91), May 1991, Toronto, Ontario, Canada 2: 933-936.Google Scholar
  14. Yuk D, Flanagan J: Telephone speech recognition using neural networks and hidden Markov models. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '99), March 1999, Phoenix, Ariz, USA 1: 157-160.Google Scholar
  15. Li W, Takeda K, Itakura F: Adaptive log-spectral regression for in-car speech recognition using multiple distributed microphones. IEEE Signal Processing Letters 2005,12(4):340-343.View ArticleGoogle Scholar
  16. Kawaguchi N, Matsubara S, Iwa H, et al.: Construction of speech corpus in moving car environment. Proceedings of the 6th International Conference of Spoken Language Processing (ICSLP '00), October 2000, Beijing, China 362-365.Google Scholar
  17. Haykin S: Neural Networks—A Comprehensive Foundation. Prentice-Hall, Englewood Cliffs, NJ, USA; 1999.MATHGoogle Scholar
  18. Quackenbush SR, Barnwell TP, Clements MA: Objective Measures of Speech Quality. Prentice-Hall, Englewood Cliffs, NJ, USA; 1988.Google Scholar
  19. Porter JE, Boll SF: Optimal estimators for spectral restoration of noisy speech. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '84), 1984, San Diego, Calif, USA 2: 18A.2.1-18A.2.4.Google Scholar
  20. Li W, Itou K, Takeda K, Itakura F: Two-stage noise spectra estimation and regression based in-car speech recognition using single distant microphone. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '05), March 2005, Philadelphia, Pa, USA I: 533-536.Google Scholar
  21. Berouti M, Schwartz R, Makhoul J: Enhancement of speech corrupted by acoustic noise. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '79), April 1979, Washington, DC, USA 4: 208-211.View ArticleGoogle Scholar
  22. Chen J, Paliwal KK, Nakamura S: Sub-band based additive noise removal for robust speech recognition. Proceedings of the 7th European Conference on Speech Communication and Technology (EUROSPEECH '01), September 2001, Aalborg, Denmark 571-574.Google Scholar
  23. Ephraim Y, Malah D: Speech enhancement using a minimum mean-square error-log-spectral amplitude estimator. IEEE Transactions on Acoustics, Speech, and Signal Processing 1985,33(2):443-445. 10.1109/TASSP.1985.1164550View ArticleGoogle Scholar
  24. Ephraim Y, Malah D: Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator. IEEE Transactions on Acoustics, Speech, and Signal Processing 1984,32(6):1109-1121. 10.1109/TASSP.1984.1164453View ArticleGoogle Scholar
  25. Cappe O, Laroche J: Evaluation of short-time spectral attenuation techniques for the restoration of musical recordings. IEEE Transactions on Speech and Audio Processing 1995,3(1):84-93. 10.1109/89.365378View ArticleGoogle Scholar
  26. Martin R: Speech enhancement using MMSE short time spectral estimation with Gamma distributed speech priors. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '02), May 2002, Orlando, Fla, USA 1: 253-256.Google Scholar
  27. Li W, Itou K, Takeda K, Itakura F: Subjective and objective quality assessment of regression-enhanced speech in real car environments. Proceedings of the 9th European Conference on Speech Communication and Technology, September 2005, Lisbon, Portugal 2093-2096.Google Scholar
  28. Carey MJ, Parris ES, Lloyd-Thomas H: A comparison of features for speech, music discrimination. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '99), March 1999, Phoenix, Ariz, USA 1: 149-152.View ArticleGoogle Scholar
  29. Peltonen V, Tuomi J, Klapuri A, Huopaniemi J, Sorsa T: Computational auditory scene recognition. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '02), May 2002, Orlando, Fla, USA 2: 1941-1944.Google Scholar
  30. Duda RO, Hart PE, Stork DG: Pattern Classification. 2nd edition. John Wiley & Sons, New York, NY, USA; 2001.MATHGoogle Scholar
  31. Shimizu Y, Kajita S, Takeda K, Itakura F: Speech recognition based on space diversity using distributed multi-microphone. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '00), June 2000, Istanbul, Turkey 3: 1747-1750.Google Scholar
  32. Deng L, Acero A, Plumpe M, Huang X: Large-vocabulary speech recognition under adverse acoustic environments. Proceedings of the 6th International Conference of Spoken Language Processing (ICSLP '00), October 2000, Beijing, China 806-809.Google Scholar
  33. Droppo J, Deng L, Acero A: Evaluation of the SPLICE algorithm on the Aurora2 database. Proceedings of the 7th European Conference on Speech Communication and Technology (EUROSPEECH '01), September 2001, Aalborg, Denmark 217-220.Google Scholar
  34. “Speech processing, transmission and quality aspects (STQ); distributed speech recognition; advanced frontend feature extraction algorithm; compression algorithm,” ETSI ES 202 050 v1.1.1, 2002.Google Scholar
  35. Griffiths LJ, Jim CW: An alternative approach to linearly constrained adaptive beamforming. IEEE Transactions on Antennas and Propagation 1982,30(1):27-34. 10.1109/TAP.1982.1142739View ArticleGoogle Scholar
  36. Haykin S: Adaptive Filter Theory. Prentice-Hall, Englewood Cliffs, NJ, USA; 2002.MATHGoogle Scholar
  37. Mendel JM: Lessons in Estimation Theory for Signal Processing, Communications, and Control. Prentice-Hall, Englewood Cliffs, NJ, USA; 1995.MATHGoogle Scholar

Copyright

© Weifeng Li et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.