An algorithm that improves speech intelligibility in noise for normal-hearing listeners

Journal of the Acoustical Society of America

Volumn 126, Issue 3, 2009, Pages 1486-1494

  Kim, Gibak ^a   Lu, Yang ^a   Hu, Yi ^a   Loizou, Philipos C ^a  

^a The University of Texas at Dallas   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN CLASSIFIER; BINARY DECISION; HUMAN LISTENERS; IDEAL BINARY MASK; INPUT SIGNAL; LOW SIGNAL-TO-NOISE RATIO; NORMAL-HEARING LISTENERS; SPEECH QUALITY; SUPPRESSION ALGORITHM; TIME FREQUENCY;

ACOUSTIC INTENSITY; ALGORITHMS; AUDITION; SIGNAL TO NOISE RATIO;

SPEECH INTELLIGIBILITY;

ALGORITHM; ARTICLE; AUDITORY STIMULATION; BAYES THEOREM; FEMALE; HEARING; HUMAN; MALE; NOISE REDUCTION; PRIORITY JOURNAL; SIGNAL NOISE RATIO; SPEECH INTELLIGIBILITY;

ACOUSTIC STIMULATION; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DATABASES AS TOPIC; FEMALE; HUMANS; MALE; NOISE; PATTERN RECOGNITION, PHYSIOLOGICAL; PERCEPTUAL MASKING; PSYCHOACOUSTICS; SOUND SPECTROGRAPHY; SPEECH; SPEECH INTELLIGIBILITY; SPEECH PERCEPTION;

EID: 70349093614     PISSN: 00014966     EISSN: None     Source Type: Journal    
DOI: 10.1121/1.3184603     Document Type: Article

References (35)

1. Bregman, A. S. (1990). Auditory Scene Analysis (MIT, Cambridge, MA).
2. Brungart, D., Chang, P., Simpson, B., and Wang, D. (2006). " Isolating the energetic component of speech-on-speech masking with ideal time-frequency segregation.," J. Acoust. Soc. Am. 120, 4007-4018. 10.1121/1.2363929 (Pubitemid 44888096)
3. Cooke, M., Green, P., Josifovski, L., and Vizinho, A. (2001). " Robust automatic speech recognition with missing and unreliable acoustic data.," Speech Commun. 34, 267-285. 10.1016/S0167-6393(00)00034-0 (Pubitemid 32284867)
4. Davis, S. B., and Mermelstein, P. (1980). " Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences.," IEEE Trans. Acoust., Speech, Signal Process. ASSP-28, 357-336. 10.1109/TASSP.1980.1163420
5. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). " Maximum likelihood from incomplete data via the EM algorithm.," J. R. Stat. Soc. Ser. B (Methodol.) 39, 1-38.
6. Ephraim, Y., and Malah, D. (1984). " Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator.," IEEE Trans. Acoust., Speech, Signal Process. ASSP-32, 1109-1121. 10.1109/TASSP.1984. 1164453 (Pubitemid 15159457)
7. Furui, S. (1986). " Speaker independent isolated word recognition using dynamic features of speech spectrum.," IEEE Trans. Acoust., Speech, Signal Process. ASSP-34, 52-59. 10.1109/TASSP.1986.1164788
8. Hu, G., and Wang, D. L. (2004). " Monaural speech segregation based on pitch tracking and amplitude modulation.," IEEE Trans. Neural Netw. 15, 1135-1150. 10.1109/TNN.2004.832812
9. Hu, G., and Wang, D. L. (2008). " Segregation of unvoiced speech from nonspeech interference.," J. Acoust. Soc. Am. 124, 1306-1319. 10.1121/1.2939132
10. Hu, Y., and Loizou, P. C. (2007a). " A comparative intelligibility study of single-microphone noise reduction algorithms.," J. Acoust. Soc. Am. 122, 1777-1786. 10.1121/1.2766778 (Pubitemid 47560539)
11. Hu, Y., and Loizou, P. C. (2007b). " Subjective evaluation and comparison of speech enhancement algorithms.," Speech Commun. 49, 588-601. 10.1016/j.specom.2006.12.006 (Pubitemid 47031352)
12. Hu, Y., and Loizou, P. C. (2008). " Techniques for estimating the ideal binary mask.," in The 11th International Workshop on Acoustic Echo and Noise Control, Seattle, WA
13. IEEE (1969). " IEEE recommended practice for speech quality measurements.," IEEE Trans. Audio Electroacoust. 17, 225-246. 10.1109/TAU.1969.1162058
14. Kollmeier, B., and Koch, R. (1994). " Speech enhancement based on physiological and psychoacoustical models of modulation perception and binaural interaction.," J. Acoust. Soc. Am. 95, 1593-1602. 10.1121/1.408546
15. Langner, G., and Schreiner, C. (1988). " Periodicity coding in the inferior colliculus of the cat. I. Neuronal mechanisms.," J. Neurophysiol. 60, 1799-1822. (Pubitemid 19017451)
16. Li, N., and Loizou, P. C. (2008a). " Effect of spectral resolution on the intelligibility of ideal binary masked speech.," J. Acoust. Soc. Am. 123, EL59-EL64. 10.1121/1.2884086
17. Li, N., and Loizou, P. C. (2008b). " Factors influencing intelligibility of ideal binary-masked speech: Implications for noise reduction.," J. Acoust. Soc. Am. 123, 1673-1682. 10.1121/1.2832617
18. Lim, J. S. (1978). " Evaluation of a correlation subtraction method for enhancing speech degraded by additive white noise.," IEEE Trans. Acoust., Speech, Signal Process. 26, 471-472. 10.1109/TASSP.1978.1163129
19. Lippmann, R. P. (1997). " Speech recognition by machines and humans.," Speech Commun. 22, 1-15. 10.1016/S0167-6393(97)00021-6
20. Loizou, P. C. (2007). Speech Enhancement: Theory and Practice (CRC, Boca Raton, FL).
21. Macmillan, N., and Creelman, D. (2005). Detection Theory: A User's Guide (Lawrence Erlbaum Associates, New York).
22. Moore, B. (2003). An Introduction to the Psychology of Hearing (Academic, London).
23. Nordqvist, P., and Leijon, A. (2004). " An efficient robust sound classification algorithm for hearing aids.," J. Acoust. Soc. Am. 115, 3033-3041. 10.1121/1.1710877 (Pubitemid 38781236)
24. Rabiner, L. (2003). " The power of speech.," Science 301, 1494-1495. 10.1126/science.1088904 (Pubitemid 37128532)
25. Reynolds, D., and Rose, R. (1995). " Robust text-independent speaker identification using Gaussian mixture speaker models.," IEEE Trans. Speech Audio Process. 3, 72-83. 10.1109/89.365379
26. Reynolds, D., Quatieri, T., and Dunn, R. (2000). " Speaker verification using adapted Gaussian mixture models.," Digit. Signal Process. 10, 19-41. 10.1006/dspr.1999.0361 (Pubitemid 30592166)
27. Scalart, P., and Filho, J. (1996). " Speech enhancement based on a priori signal to noise estimation.," in Proceedings of the IEEE International Conference on Acoustics, Speech, Signal Processing, pp. 629-632.
28. Scharenborg, O. (2007). " Reaching over the gap: A review of efforts to link human and automatic speech recognition research.," Speech Commun. 49, 336-347. 10.1016/j.specom.2007.01.009 (Pubitemid 46670364)
29. Seltzer, M., Raj, B., and Stern, R. (2004). " A Bayesian classifier for spectrographic mask estimation for missing feature speech recognition.," Speech Commun. 43, 379-393. 10.1016/j.specom.2004.03.006
30. Sroka, J. J., and Braida, L. D. (2005). " Human and machine consonant recognition.," Speech Commun. 45, 401-423. 10.1016/j.specom.2004. 11.009 (Pubitemid 40423287)
31. Tchorz, J., and Kollmeier, B. (2003). " SNR estimation based on amplitude modulation analysis with applications to noise suppression.," IEEE Trans. Speech Audio Process. 11, 184-192. 10.1109/TSA.2003.811542
32. Varga, A., and Steeneken, H. J. M. (1993). " 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. 12, 247-251. 10.1016/0167-6393(93)90095-3
33. Wang, D. L., and Brown, G. J. (2006). Computational Auditory Scene Analysis: Principles, Algorithms, and Applications (Wiley, Hoboken, NJ).
34. Zakis, J. A., Dillon, H., and McDermott, H. J. (2007). " The design and evaluation of a hearing aid with trainable amplification parameters.," Ear Hear. 28, 812-830. 10.1097/AUD.0b013e3181576738 (Pubitemid 350059322)
35. Zeng, F.-G., Nie, K., Stickney, G. S., Kong, Y.-Y., Vongphoe, M., Bhargave, A., Wei, C., and Cao, K. (2005). " Speech recognition with amplitude and frequency modulations.," Proc. Natl. Acad. Sci. U.S.A. 102, 2293-2298. 10.1073/pnas.0406460102 (Pubitemid 40279369)