A feature study for classification-based speech separation at low signal-to-noise ratios

IEEE/ACM Transactions on Audio Speech and Language Processing

Volumn 22, Issue 12, 2014, Pages 1993-2002

  Chen, Jitong ^a   Wang, Yuxuan ^a   Wang, Deliang ^a  

^a The Ohio State University   (United States)

Author keywords

ARMA filtering; Classification; Multi resolution cochleagram; Speech separation

Indexed keywords

ACOUSTIC NOISE; CLASSIFICATION (OF INFORMATION); SEPARATION; SPEECH; SPEECH ANALYSIS; SPEECH INTELLIGIBILITY; SPEECH RECOGNITION;

ARMA FILTERING; AUTOMATIC SPEECH RECOGNITION; AUTOREGRESSIVE MOVING AVERAGE; COCHLEAGRAM; CONTEXTUAL INFORMATION; LOW SIGNAL-TO-NOISE RATIO; POST-PROCESSING TECHNIQUES; SPEECH SEPARATION;

SIGNAL TO NOISE RATIO;

EID: 84921769616     PISSN: 23299290     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASLP.2014.2359159     Document Type: Article

References (42)

1. M. Ahmadi, V. L. Gross, and D. G. Sinex, "Perceptual learning for speech in noise after application of binary time-frequency masks," J.Acoust. Soc. Amer., vol. 133, pp. 1687-1692, 2013.
2. J. Barker, E. Vincent, N. Ma, H. Christensen, and P. Green, "The PASCAL CHiME speech separation and recognition challenge," Comput. Speech Lang., vol. 27, pp. 621-633, 2013.
3. A. S. Bregman, "Auditory scene analysis," in The Perceptual Organization of Sound. Cambridge, MA, USA: MIT Press, 1994.
4. D. S. Brungart, P. S. Chang, B. D. Simpson, and D. L. Wang, "Isolating the energetic component of speech-on-speech masking with ideal timefrequency segregation," J. Acoust. Soc. Amer., vol. 120, pp.4007-4018, 2006.
5. C. 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.
6. J. Chen, Y. Wang, and D. L. Wang, "A feature study for classification- based speech separation at very low signal-to-noise ratio," in Proc.ICASSP, 2014, pp. 7039-7043.
7. S. Gonzalez and M. Brookes, "A pitch estimation filter robust to high levels of noise (PEFAC)," in Proc. Euro. Sig. Process. Conf., 2011, pp.451-455.
8. K. Han and D. L. Wang, "A classification based approach to speech segregation," J. Acoust. Soc. Amer., vol. 132, pp. 3475-3483, 2012.
9. E. W. Healy, S. E. Yoho, Y. Wang, and D. L. Wang, "An algorithm to improve speech recognition in noise for hearing-impaired listeners," J.Acoust. Soc. Amer., vol. 134, pp. 3029-3038, 2013.
10. H. Hermansky, "Perceptual linear predictive (PLP) analysis of speech," J. Acoust. Soc. Amer., vol. 87, pp. 1738-1752, 1990.
11. H. Hermansky and N. Morgan, "RASTA processing of speech," IEEE Trans. Speech, Audio Process., vol. 2, no. 4, pp. 578-589, Oct. 1994.
12. G. Hu and D. L. Wang, "Auditory segmentation based on onset and offset analysis," IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 2, pp. 396-405, Feb. 2007.
13. G. Hu and D. L. Wang, "Segregation of unvoiced speech from nonspeech interference," J. Acoust. Soc. Amer., vol. 124, pp. 1306-1319, 2008.
14. G. Hu and D. L. Wang, "A tandem algorithm for pitch estimation and voiced speech segregation," IEEE Trans. Audio, Speech, Lang.Process., vol. 18, no. 8, pp. 2067-2079, Nov. 2010.
15. Y. Hu and P. C. Loizou, "A comparative intelligibility study of singlemicrophone noise reduction algorithms," J. Acoust. Soc. Amer., vol.122, pp. 1777-1786, 2007.
16. IEEE, "IEEE recommended practice for speech quality measurements," IEEE Trans. Audio Electroacoust., vol. 17, pp. 225-246, 1969.
17. S. Ikbal, H. Misra, and H. Bourlard, "Phase autocorrelation (PAC) derived robust speech features," in Proc. ICASSP, 2003, pp. 133-136.
18. Z. Jin and D. L. Wang, "A supervised learning approach to monaural segregation of reverberant speech," IEEE Trans. Audio, Speech, Lang.Process., vol. 17, no. 4, pp. 625-638, May 2009.
19. C. Kim and R. Stern, "Power-normalized cepstral coefficients (PNCC) for robust speech recognition," in Proc. ICASSP, 2012, pp. 4101-4104.
20. C. Kim and R. M. Stern, "Nonlinear enhancement of onset for robust speech recognition," in Proc. Interspeech, 2010, pp. 2058-2061.
21. 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.
22. G. Kim, Y. Lu, Y. Hu, and P. C. Loizou, "An algorithm that improves speech intelligibility in noise for normal-hearing listeners," J. Acoust.Soc. Amer., vol. 126, pp. 1486-1494, 2009.
23. K. Kumar, C. Kim, and R. M. Stern, "Delta-spectral cepstral coefficients for robust speech recognition," in Proc. ICASSP, 2011, pp.4784-4787.
24. N. Li and P. C. Loizou, "Factors influencing intelligibility of ideal binary- masked speech: Implications for noise reduction," J. Acoust. Soc.Amer., vol. 123, pp. 1673-1682, 2008.
25. P. C. Loizou, "Speech enhancement," in theory and practice. Boca Raton, FL, USA: CRC, 2007.
26. H. K.Maganti and M.Matassoni, "An auditory basedmodulation spectral feature for reverberant speech recognition," in Proc. Interspeech, 2010, pp. 570-573.
27. L. Meier, S. V. D. Geer, and P. Bühlmann, "The group lasso for logistic regression," J. Roy. Stat. Soc. Ser. B, vol. 70, pp. 53-71, 2008.
28. B. C. Moore, Cochlear hearing loss: Physiological, psychological and technical issues. Sussex, U.K.: Wiley, 2007.
29. S. K. Nemala, K. Patil, and M. Elhilali, "A multistream feature framework based on bandpass modulation filtering for robust speech recognition," IEEE Trans. Audio, Speech, Lang. Process., vol. 21, no. 2, pp.416-426, Feb. 2013.
30. R. Patterson, I. Nimmo-Smith, J. Holdsworth, and P. Rice, "An efficient auditory filterbank based on the gammatone function," Applied Psychology Unit Rep. 2341, 1988.
31. M. R. Schädler, B. T. Meyer, and B. Kollmeier, "Spectro-temporal modulation subspace-spanning filter bank features for robust automatic speech recognition," J. Acoust. Soc. Amer., vol. 131, pp. 4134-4151, 2012.
32. B. J. Shannon and K. K. Paliwal, "Feature extraction from higher lag autocorrelation coefficients for robust speech recognition," Speech Commun., vol. 48, pp. 1458-1485, 2006.
33. Y. Shao and D. L.Wang, "Robust speaker identification using auditory features and computational auditory scene analysis," in Proc. ICASSP, 2008, pp. 1589-1592.
34. A. Varga and H. J. Steeneken, "Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems," Speech Commun., vol. 12, pp. 247-251, 1993.
35. D. L. Wang, "On ideal binary mask as the computational goal of auditory scene analysis," in Speech separation by humans and machines, P. Divenyi, Ed. Boston, MA, USA: Kluwer, 2005, pp. 181-197.
36. D. L.Wang and G. J. Brown, "Computational auditory scene analysis," in Principles, algorithms and applications.. Hoboken, NJ, USA: Wiley-IEEE Press, 2006.
37. D. L. Wang, U. Kjems, M. S. Pedersen, J. B. Boldt, and T. Lunner, "Speech intelligibility in background noise with ideal binary time-frequency masking," J. Acoust. Soc. Amer., vol. 125, pp. 2336-2347, 2009.
38. Y. Wang, K. Han, and D. L. Wang, "Exploring monaural features for classification-based speech segregation," IEEE Trans. Audio, Speech, Lang. Process., vol. 21, no. 2, pp. 270-279, Feb. 2013.
39. Y. Wang and D. L. Wang, "Towards scaling up classification-based speech separation," IEEE Trans. Audio, Speech, Lang. Process., vol.21, no. 7, pp. 1381-1390, Jul. 2013.
40. K. Yuo and H. Wang, "Robust features for noisy speech recognition based on temporal trajectory filtering of short-time autocorrelation sequences," Speech Commun., vol. 28, pp. 13-24, 1999.
41. X.-L. Zhang and D. L.Wang, "Boosted deep neural networks and multiresolution cochleagram features for voice activity detection," in Proc.Interspeech, 2014, pp. 1534-1538.
42. X. Zhao, Y. Shao, and D. L.Wang, "CASA-based robust speaker identification," IEEE Trans. Audio, Speech, Lang. Process., vol. 20, no. 5, pp. 1608-1616, Jul. 2012.