Sparse Auditory Reproducing Kernel (SPARK) features for noise-robust speech recognition

IEEE Transactions on Audio, Speech and Language Processing

Volumn 20, Issue 4, 2012, Pages 1362-1371

  Fazel, Amin ^a   Chakrabartty, Shantanu ^a  

^a Michigan State University   (United States)

Author keywords

Auditory HMAX; gammatone functions; reproducing kernel Hilbert space (RKHS); robust speech recognition; sparse features

Indexed keywords

AUDITORY HMAX; BASIS FUNCTIONS; COMPUTATIONALLY EFFICIENT; DATA SETS; FEATURE EXTRACTION ALGORITHMS; FEATURE PRUNING; KERNEL FUNCTION; NOISE ROBUST SPEECH RECOGNITION; OVER-COMPLETE; REPRODUCING KERNEL; REPRODUCING KERNEL HILBERT SPACES; ROBUST SPEECH RECOGNITION; SPARSE FEATURES; SPEECH FEATURES; SPEECH RECOGNIZER; SPEECH SIGNALS;

SPEECH RECOGNITION;

ELECTRIC SPARKS;

EID: 84857464869     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2011.2179294     Document Type: Article

References (64)

1. Y. Gong, "Speech recognition in noisy environments: A survey," Speech Commun., vol. 16, pp. 261-291, Apr. 1995.
2. B. H. Juang and T. H. Chen, "The past, present, and future of speech processing," IEEE Signal Process. Mag., vol. 15, pp. 24-48, May 1998.
3. S. F. Boll, "Suppression of acoustic noise in speech using spectral subtraction," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-27, no. 4, pp. 113-120, Apr. 1979.
4. A. Sankar and C.-H. Lee, "Robust speech recognition based on stochastic matching," in Proc. ICASSP, 1995, pp. 121-124.
5. M. G. Rahim and B.-H. Juang, "Signal bias removal by maximum likelihood estimation for robust telephone speech recognition," IEEE Trans. Speech Audio Process, vol. 4, no. 1, pp. 19-30, Jan. 1996. (Pubitemid 126752986)
6. C. J. Leggetter and P. C. Woodland, "Maximum likelihood linear regression for speaker adaptation of continuous density HMMs," Comput. Speech Lang., vol. 9, pp. 171-185, Apr. 1995.
7. M. J. F. Gales and S. Young, "An improved approach to the hidden Markov model decomposition of speech and noise," in Proc. ICASSP, 1992, pp. 233-236.
8. A. Acero, L. Deng, T. Kristjansson, and J. Zhang, "HMM adaptation using vector Taylor series for noisy speech recognition," in Proc. ICSLP, 2000, pp. 869-872.
9. Y. Gong, "A method of joint compensation of additive and convolutive distortions for speaker-independent speech recognition," IEEE Trans. Speech Audio Process., vol. 13, no. 5, pp. 975-983, Sep. 2005. (Pubitemid 41558911)
10. J. Li, L. Deng, D. Yu, Y. Gong, and A. Acero, "A unified framework of HMM adaptation with joint compensation of additive and convolutive distortions," Comput. Speech Lang., vol. 23, pp. 389-405, Jul. 2009.
11. H. Liao and M. J. F. Gales, "Adaptive training with joint uncertainty decoding for robust recognition of noisy data," in Proc. ICASSP, 2007, pp. 389-392.
12. J. Li,L.Deng,Y.Gong, andA.Acero, "High-performance HMM adaptation with joint compensation of additive and convolutive distortions via vector Taylor series," in Proc. ASRU, 2007, pp. 65-70.
13. O.Kalinli,M. L. Seltzer, andA.Acero, "Noise adaptive training using a vector Talyor series approach for noise robust automatic speech recognition," in Proc. ICASSP, 2009, pp. 3825-3828.
14. L. R. Rabiner and B. H. Juang, Fundamentals of Speech Recognition. Englewood Cliffs, NJ: Prentice-Hall, 1993.
15. K. T. Assaleh and R. J. Mammone, "New LP-derived features for speaker identification," IEEE Trans. Speech Audio Process., vol. 2, no. 4, pp. 630-638, Oct. 1994.
16. S. Furui, "Cepstral analysis techniques for automatic speaker verification," IEEE Trans. Acoust., Speech, Signal Process., vol. 29, no. 2, pp. 254-272, Apr. 1981. (Pubitemid 11495877)
17. H. Hermansky, "Perceptual linear predictive (PLP) analysis for speech," J. Acoust. Soc. Amer., vol. 87, pp. 1738-1752, Apr. 1990. (Pubitemid 20256470)
18. B. Atal, "Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification," J. Acoust. Soc. Amer., vol. 55, pp. 1304-1322, 1974.
19. S. Molau, F. Hilger, and H. Ney, "Feature space normalization in adverse acoustic conditions," in Proc. ICASSP, 2003, pp. 656-659.
20. H. Hermansky and N. Morgan, "Rasta processing of speech," IEEE Trans. Speech Audio Process, vol. 2, no. 4, pp. 578-589, Oct. 1994.
21. M. Padmanabhan and S. Dharanipragada, "Maximum likelihood nonlinear transformation for environment adaptation in speech recognition systems," in Proc. Eurospeech, 2001, pp. 2359-2362.
22. D. Macho, L. Mauuary, B. Noe, Y. M. Cheng, D. Ealey, D. Jouvet, H. Kelleher, D. Pearce, and F. Saadoun, "Evaluation of a noise robust DSR front-end on Aurora databases," in Proc. ICSLP, 2002, pp. 17-20.
23. "Speech Processing, Transmission and Quality Aspects (STQ); Distributed Speech Recognition; Advanced Front-End Feature Extraction Algorithm; Compression Algorithms," 2007, ETSI ES 202 050 Vers. 1.1.5.
24. L. Deng, A. Acero, M. Plumpe, and X. Huang, "Large vocabulary speech recognition under adverse acoustic environments," in Proc. ICSLP, 2000, pp. 806-809.
25. C. Kim and R. M. Stern, "Feature extraction for robust speech recognition using a power-law nonlinearity and power-bias subtraction," in Proc. Interspeech, 2009, pp. 28-31.
26. O. Ghitza, "Auditory nerve representation as a front-end for speech recognition in a noisy environment," Comput. Speech Lang., vol. 1, pp. 109-131, 1986.
27. Y. Shao, S. Srinivasan, Z. Jin, and D. L. Wang, "A computational auditory scene analysis system for speech segregation and robust speech recognition," Comput. Speech Lang., vol. 24, 2010.
28. Q. Li and Y. Huang, "Robust speaker identification using an auditory-based feature," in Proc. ICASSP, 2010, pp. 4514-4517.
29. S. Chatterjee and W. B. Kleijn, "Auditory model based design and optimization of feature vectors for automatic speech recognition," IEEE Trans. Audio, Speech, Lang. Process., vol. 19, no. 6, pp. 1813-1825, Aug. 2011.
30. M. Holmberg, D. Gelbart, and W. Hemmert, "Automatic speech recognition with an adaptation model motivated by auditory processing," IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 1, pp. 43-49, Jan. 2006. (Pubitemid 43863451)
31. J. Tchorz and B. Kollmeier, "A model of auditory perception as front end for automatic speech recognition," J. Acoust. Soc. Amer., vol. 106, pp. 2040-2050, Oct. 1999.
32. 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, no. 5, pp. 451-464, Sep. 1997. (Pubitemid 127746017)
33. D. S. Kim, S. Y. Lee, and R. M. Kil, "Auditory processing of speech signals for robust speech recognition in real-world noisy environments," IEEE Trans. Speech Audio Process., vol. 7, no. 1, pp. 55-69, Jan. 1999.
34. S. Chakrabartty, Y. Deng, and G. Cauwenberghs, "Robust speech feature extraction by growth transformation in reproducing kernel Hilbert space," IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 6, pp. 1842-1849, Aug. 2007.
35. A. Fazel and S. Chakrabartty, "Non-linear filtering in reproducing kernel Hilbert spaces for noise-robust speaker verification," in Proc. ISCAS, 2009, pp. 113-116.
36. B. A. Olshausen and D. J. Field, "Emergence of simple-cell receptive field properties by learning a sparse code for natural images," Nature, vol. 381, pp. 607-609, Jun. 1996. (Pubitemid 26177476)
37. E. C. Smith and M. S. Lewicki, "Efficient coding of time-relative structure using spikes," Neural Comput., vol. 17, pp. 19-45, Jan. 2005. (Pubitemid 40305881)
38. E. C. Smith and M. S. Lewicki, "Efficient auditory coding," Nature, vol. 439, pp. 978-982, Feb. 2006. (Pubitemid 43292416)
39. R. Patterson and B. Moore, "Auditory filters and excitation patterns as representations of frequency resolution," Freq. Select. in Hear., pp. 123-177, 1986.
40. T. Chi, P. Ru, and S. Shamma, "Multiresolution spectrotemporal analysis of complex sounds," J. Acoust. Soc. Amer., vol. 118, pp. 887-906, 2005. (Pubitemid 41129224)
41. C. M. Wessinger, J. VanMeter, B. Tian, J. V. Lare, J. Pekar, and J. P. Rauschecker, "Hierarchical organization of the human auditory cortex revealed by functional magnetic resonance imaging," J. Cognitive Neu-rosci., vol. 13, pp. 1-7, 2001. (Pubitemid 32121491)
42. K. Okada, F. Rong, J. Venezia, W. Matchin, I.-H. Hsieh, K. Saberi, J. T. Serences, and G. Hickok, "Hierarchical organization of human auditory cortex: Evidence from acoustic invariance in the response to intelligible speech," Cereb. Cortex, vol. 20, pp. 2486-2495, 2010.
43. A. Boemio, S. Fromm, A. Braun, and D. Poeppel, "Hierarchical and asymmetric temporal sensitivity in human auditory cortices," Nature Neurosci., vol. 8, pp. 389-395, 2005. (Pubitemid 40300197)
44. F. Theunissen, K. Sen, and A. J. Doupe, "Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds," J. Neurosci., vol. 20, no. 6, pp. 2315-2331, 2000. (Pubitemid 30230085)
45. J. F. Linden, R. C. Liu, M. Sahani, C. E. Schreiner, and M. M. Merzenich, "Spectrotemporal structure of receptive fields in areas ai and aaf of mouse auditory cortex," J. Neurophysiol., vol. 90, no. 4, pp. 2660-2675, 2003. (Pubitemid 37266531)
46. M.Kleinschmidt and D. Gelbart, "Improving word accuracy with gabor feature extraction," in Proc. ICSLP, 2002.
47. M. Kleinschmidt, "Localized spectro-temporal features for automatic speech recognition," in Proc. Eurospeech, 2003.
48. N. Mesgarani, M. Slaney, and S. A. Shamma, "Discrimination of speech from nonspeech based on multiscale spectro-temporal modulations," IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 3, pp. 920-930, May 2006. (Pubitemid 46547653)
49. T. Ezzat and T. Poggio, "Discriminative word-spotting using ordered spectro-temporal patch features," in Proc. SAPA Workshop, Sep. 2008, pp. 35-40.
50. J. Bouvrie, T. Ezzat, and T. Poggio, "Localized spectro-temporal cep-stral analysis of speech," in Proc. ICASSP, May 2008, pp. 4733-4736.
51. M. Riesenhuber and T. Poggio, "Hierarchical models of object recognition in cortex," Nature Neurosci., vol. 2, pp. 1019-1025, 1999. (Pubitemid 30599567)
52. J. Bouvrie, T. Poggio, L. Rosasco, S. Smale, and A. Wibisono, Generalization and Properties of the Neural Response Mass. Inst. of Technol., 2010, Tech. Rep., MIT-CSAIL-TR-2010-051, CBCL-292.
53. L. Liu, J. He, and G. Palm, "Effects of phase on the perception of intervocalic stop consonants,"Speech Commun., vol. 22, no. 4,pp. 403-417, 1997. (Pubitemid 127433607)
54. R. Schluter and H. Ney, "Using phase spectrum information for improved speech recognition performance," in Proc. ICASSP, 2001, pp. 133-136. (Pubitemid 32839205)
55. M. Slaney, An efficient implementation of the Patterson-Holdsworth auditory filter bank, Apple Computer Tech. Rep., 1993, no. 35.
56. B. R. Glasberg and B. C. J. Moore, "Derivation of auditory filter shapes from notched-noise data," Hear. Res., vol. 47, pp. 103-108, 1990.
57. R. D. Patterson, Holdsworth, I. Nimmo-Smith, and P. Rice, "SVOS final report: The auditory filterbank," APU Rep., 1988, no. 2341.
58. G. Wahba, "Splines models for observational data," inSeries in Applied Mathematics. Philadelphia, PA: SIAM, 1990, vol. 59.
59. F. Girosi, M. Jones, and T. Poggio, "Regularization theory and neural networks architectures," Neural Comput., vol. 7, pp. 219-269, 1995.
60. R. Schluter, L. Bezrukov, H. Wagner, and H. Ney, "Gammatone features and feature combination for large vocabulary speech recognition," in Proc. ICASSP, 2007, pp. 649-652.
61. H. G Hirsch and D. Pearce, "The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions," in Proc. ASR, 2000, pp. 181-188.
62. "Transmission Performance Characteristics of Pulse Code Modulation Channels," 1996, ITU-T Recommendation G.712.
63. "HTK Speech Recognition Toolkit," 2011 [Online]. Available: htk.eng.cam.ac.uk/
64. "Speech Processing, Transmission and Quality Aspects (STQ); Distributed Speech Recognition; Front-End Feature Extraction Algorithm; Compression Algorithms," 2003, ETSI ES 201 108 Version 1.1.3.