Binary and ratio time-frequency masks for robust speech recognition

Speech Communication

Volumn 48, Issue 11, 2006, Pages 1486-1501

  Srinivasan, Soundararajan ^a   Roman, Nicoleta ^b   Wang, DeLiang ^b  

^a OHIO STATE UNIVERSITY   (United States)

^b Ohio State University   (United States)

Author keywords

Binaural processing; Ideal binary mask; Missing data recognizer; Ratio mask; Robust speech recognition; Speech segregation

Indexed keywords

DATA REDUCTION; ROBUSTNESS (CONTROL SYSTEMS); SPEECH COMMUNICATION; TIME DOMAIN ANALYSIS;

BINAURAL PROCESSING; IDEAL BINARY MASK; MISSING-DATA RECOGNIZER; RATIO MASK; ROBUST SPEECH RECOGNITION; SPEECH SEGREGATION; SPEECH SIGNALS;

SPEECH RECOGNITION;

EID: 33750311718     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.specom.2006.09.003     Document Type: Article

References (43)

1. Barker, J., Josifovski, L., Cooke, M., Green, P., 2000. Soft decisions in missing data techniques for robust automatic speech recognition. In: Proc. International Conference on Spoken Language Processing '00, pp. 373-376.
2. Blauert J. Spatial Hearing - The Psychophysics of Human Sound Localization (1997), MIT Press, Cambridge, MA
3. Boll S.F. Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans. Acoust. Speech Signal Processing ASSP-27 2 (1979) 113-120
4. In: Bradstein M., and Ward D. (Eds). Microphone Arrays: Signal Processing Techniques and Applications (2001), Springer, Berlin, Germany
5. Brown G.J., and Wang D.L. Separation of speech by computational auditory scene analysis. In: Benesty J., Makino S., and Chen J. (Eds). Speech Enhancement (2005), Springer, New York 371-402
6. Cardoso J.F. Blind signal separation: statistical principles. Proc. IEEE 86 10 (1998) 2009-2025
7. Chen, C.-P., Bilmes, J., Ellis, D.P.W., 2005. Speech feature smoothing for robust ASR. In: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing '05, vol. 1, pp. 525-528.
8. Cole, R., Noel, M., Lander, T., Durham, T., 1995. New telephone speech corpora at CSLU. In: Proc. European Conference on Speech Communication and Technology '95, pp. 821-824.
9. Cooke M., Green P., Josifovski L., and Vizinho A. Robust automatic speech recognition with missing and unreliable acoustic data. Speech Commun. 34 (2001) 267-285
10. Cunningham, S., Cooke, M., 1999. The role of evidence and counter-evidence in speech perception. In: Proc. International Congress on Phonetic Sciences '99, pp. 215-218.
11. Davis S.B., and Mermelstein P. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. Acoust. Speech Signal Processing ASSP-28 4 (1980) 357-366
12. de Veth, J., de Wet, F., Cranen, B., Boves, L., 1999. Missing feature theory in ASR: make sure you miss the right type of features. In: Proc. Workshop on Robust Methods for Speech Recognition in Adverse Conditions '99, pp. 231-234.
13. Droppo, J., Acero, A., Deng, L., 2002. A nonlinear observation model for removing noise from corrupted speech log mel-spectral energies. In: Proc. International Conference on Spoken Language Processing '02, pp. 1569-1572.
14. Ehlers F., and Schuster H.G. Blind separation of convolutive mixtures and an application in automatic speech recognition in a noisy environment. IEEE Trans. Signal Processing 45 10 (1997) 2608-2612
15. Ephraim Y. A Bayesian estimation approach for speech enhancement using hidden Markov models. IEEE Trans. Signal Processing 40 4 (1992) 725-735
16. Ephraim Y., and Malah D. Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator. IEEE Trans. Acoust. Speech Signal Processing ASSP-32 6 (1984) 1109-1121
17. Gales M.J.F., and Young S.J. Robust continuous speech recognition using parallel model combination. IEEE Trans. Speech Audio Processing 4 (1996) 352-359
18. Gardner, W.G., Martin, K.D., 1994. HRTF measurements of a KEMAR dummy-head microphone. Technical Report #280, MIT Media Lab Perceptual Computing Group.
19. Garofolo, J., Lamel, L., Fisher, W., Fiscus, J., Pallet, D., Dahlgren, N., 1993. Darpa timit acoustic-phonetic continuous speech corpus. Technical Report NISTIR 4930, National Institute of Standards and Technology, Gaithersburg, MD.
20. Glotin, H., Berthommier, F., Tessier, E., 1999. A CASA-labelling model using the localisation cue for robust cocktail-party speech recognition. In: Proc. European Conference on Speech Communication and Technology '99, pp. 2351-2354.
21. Gong Y. Speech recognition in noisy environments: a survey. Speech Commun. 16 (1995) 261-291
22. Hughes T.B., Kim H.S., DiBase J.H., and Silverman H.F. Performance of an HMM speech recognizer using a real-time tracking microphone array as input. IEEE Trans. Speech Audio Processing 7 3 (1999) 346-349
23. Leonard, R.G., 1984. A database for speaker-independent digit recognition. In: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing '84, pp. 111-114.
24. Lippmann R.P. Speech recognition by machines and humans. Speech Commun. 22 (1997) 1-15
25. Little R.J.A., and Rubin D.B. Statistical Analysis with Missing Data (1987), Wiley, New York, NY
26. Macho, D., Mauuary, L., Noe, B., Cheng, Y.M., Ealey, D., Jouvet, D., Kelleher, H., Pearce, D., Saadoun, F., 2002. Evaluation of a noise-robust DSR front-end on aurora databases. In: Proc. International Conference on Spoken Language Processing '02, pp. 17-20.
27. McAulay R., and Malpass M.L. Speech enhancement using a soft-decision noise suppression filter. IEEE Trans. Acoust. Speech Signal Processing ASSP-28 2 (1980) 137-145
28. Oppenheim A.V., Schafer R.W., and Buck J.R. Discrete-time Signal Processing. second ed. (1999), Prentice-Hall, Upper Saddle River, NJ
29. Palomaki K.J., Brown G.J., and Wang D.L. A binaural processor for missing data speech recognition in the presence of noise and small-room reverberation. Speech Commun. 43 (2004) 361-378
30. Price, P., Fisher, W.M., Bernstein, J., Pallett, D.S., 1988. The DARPA 1000 word Resource Management database for continuous speech recognition. In: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing '88, pp. 651-654.
31. Rabiner L.R., and Juang B.H. Fundamentals of Speech Recognition. second ed. (1993), Prentice-Hall, Englewood Cliffs, NJ
32. Raj B., Seltzer M.L., and Stern R.M. Reconstruction of missing features for robust speech recognition. Speech Commun. 43 (2004) 275-296
33. Roman N., Wang D.L., and Brown G.J. Speech segregation based on sound localization. J. Acoust. Soc. Am. 114 (2003) 2236-2252
34. In: Rosenthal D.F., and Okuno H.G. (Eds). Computational Auditory Scene Analysis (1998), Lawrence Erlbaum Associates, Mahwah, NJ
35. Shire, M.L., 2000. Discriminant training of front-end and acoustic modeling stages to heterogeneous acoustic environments for multi-stream automatic speech recognition. Ph.D. thesis, University of California, Berkley.
36. Srinivasan, S., Roman, N., Wang, D.L., 2004. On binary and ratio time-frequency masks for robust speech recognition. In: Proc. International Conference on Spoken Language Processing '04, pp. 2541-2544.
37. STQ-AURORA, 2005-11. Speech processing, transmission and quality aspects (STQ); distributed speech recognition; advanced front-end feature extraction algorithm; compression algorithms. In: ETSI ES 202 050 V1.1.4. European Telecommunications Standards Institute.
38. Tessier, E., Berthommier, F., Glotin, H., Choi, S., 1999. A CASA front-end using the localisation cue for segregation and then cocktail-party speech recognition. In: Proc. IEEE International Conference on Speech Processing, pp. 97-102.
39. van Hamme, H., 2003. Robust speech recognition using missing feature theory in the cepstral or LDA domain. In: Proceedings of the European Conference on Speech Communication and Technology '03, pp. 3089-3092.
40. van Trees H.L. Detection, Estimation, and Modulation Theory, Part I (1968), Wiley, New York, NY
41. Varga, A.P., Moore, R.K., 1990. Hidden Markov model decomposition of speech and noise. In: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing '90, pp. 845-848.
42. Varga, A.P., Steeneken, H.J.M., Tomlinson, M., Jones, D., 1992. The NOISEX-92 study on the effect of additive noise on automatic speech recognition. Technical Report, Speech Research Unit, Defense Research Agency, Malvern, UK.
43. Young S., Kershaw D., Odell J., Valtchev V., and Woodland P. The HTK Book (for HTK Version 3.0) (2000), Microsoft Corporation