Robust speech detection in real acoustic backgrounds with perceptually motivated features

Speech Communication

Volumn 53, Issue 5, 2011, Pages 690-706

  Bach, Jörg Hendrik ^a,b   Anemüller, Jörn ^a   Kollmeier, Birger ^a  

^a UNIVERSITY OF OLDENBURG   (Germany)

^b NONE   (Germany)

Author keywords

Amplitude modulations; Fluctuating noise; Pattern classification; Real world scenario; Speech detection

Indexed keywords

BACKGROUND NOISE; BASE-LINE CONDITIONS; DETECTION PERFORMANCE; EXTERNAL CONDITIONS; FEATURE TYPES; FLUCTUATING NOISE; FOURIER DECOMPOSITION; GENERALISATION; HIERARCHICAL APPROACH; LINEAR KERNEL; MEL-FREQUENCY CEPSTRAL COEFFICIENTS; NONLINEAR KERNELS; PATTERN CLASSIFICATION; PERCEPTUAL LINEAR PREDICTIONS; REAL-WORLD SCENARIO; ROBUST SPEECH; SIGNAL TO NOISE; SPECTRAL FEATURE; SPECTROGRAMS; SPEECH DETECTION; SPEECH/NONSPEECH CLASSIFICATION; TRAINING AND TESTING;

AMPLITUDE MODULATION; AUDIO ACOUSTICS; DETECTORS; SIGNAL DETECTION; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO; SUPPORT VECTOR MACHINES;

SPEECH RECOGNITION;

EID: 79953670471     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.specom.2010.07.003     Document Type: Article

References (39)

1. Anemüller, J., Bach, J.H., Caputo, B., Jie, L., Ohl, F., Orabona, F., Vogels, R., Weinshall, D., Zweig, A., 2008a. Biologically motivated audio-visual cue integration for object categorization. In: First Internat. Conf. on Cognitive Systems (CogSys) (Karlsruhe, Germany).
2. Anemüller, J., Schmidt, D., Bach, J.-H., 2008b. Detection of speech embedded in real acoustic background based on amplitude modulation spectrogram features. In: Proc. Interspeech (Brisbane, Australia).
3. M.A. Bee, and G.M. Klump Primitive auditory stream segregation: a neurophysiological study in the songbird forebrain J. Neurophysiol. 92 2004 1088 1104 (Pubitemid 38931970)
4. A.S. Bregman Auditory Scene Analysis: The Perceptual Organization of Sound 1990 The MIT Press Cambridge, Massachusetts
5. M. Büchler, S. Allegro, S. Launer, and N. Dillier Sound classification in hearing aids inspired by auditory scene analysis EURASIP J. Appl. Signal Process. 18 2005 2991 3002 (Pubitemid 44796363)
6. Chang, C.C., Lin, C.J., 2001. LIBSVM: a library for support vector machines. Software available at .
7. S. Davis, and P. Mermelstein Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences IEEE Trans. Acoust. Speech Signal Process. 28 1980 357 366 (Pubitemid 11464930)
8. W.A. Dreschler, C. Ludvigson, and S. Westermann Artificial noise signals with speechlike spectral and temporal properties for hearing instrument assessment J. Acoust. Soc. Amer. 105 1999 1296
9. Ellis, D., 2006. .
10. J.S. Garofolo, L.F. Lamel, W.M. Fisher, J.G. Fiscus, D.S. Pallett, N.L. Dahlgren, and V. Zue TIMIT Acoustic-Phonetic Continuous Speech Corpus 1993 Linguistic Data Consortium Philadelphia
11. T. Gramss, and H.W. Strube Recognition of isolated words based one psychoacoustics and neurobiology Speech Commun. 9 1990 35 40 (Pubitemid 20717751)
12. Greenberg, S., Kingsbury, B., 1997. The modulation spectrogram: in pursuit of an invariant representation of speech. In: Proc. ICASSP (Munich, Germany).
13. Happel, M.F.K., Müller, S., Anemüller, J., W.Ohl, F. (2008). Predictability of STRFs in auditory cortex neurons depends on stimulus class. In Proc. InterSpeech (Brisbane, Australia), p. 670.
14. H. Hermansky Perceptual Linear Prediction (PLP) Analysis for Speech J. Acoust. Soc. Amer. 87 1990 1738 1752 (Pubitemid 20256470)
15. H. Hermansky, and N. Morgan Rasta processing of speech IEEE Trans. Speech Audio Process. 2 1994 578 589
16. ITU, I.T.U., 1996. G.729: a silence compression scheme for G.729 optimized for terminals conforming to Recommendation V.70. In: Recommendation G.x729 Annex B.
17. Kingsbury, B.E.D., Morgan, N., 1997. Recognizing reverberant speech with RASTA-PLP. In: Proc. ICASSP (Munich, Germany), pp. 1259-1262.
18. B.E.D. Kingsbury, N. Morgan, and S. Greenberg Robust speech recognition using the modulation spectrogram Speech Commun. 25 1998 117 132 (Pubitemid 128413637)
19. Kleinschmidt, M. (2002). Robust speech recognition based on spectro-temporal processing. Department of Physics, University of Oldenburg, Oldenburg.
20. B. Kollmeier, and R. Koch Speech enhancement based on physiological and psychoacoustical models of modulation perception and binaural interaction J. Acoust. Soc. Amer. 95 1994 1593 1602 (Pubitemid 24085760)
21. G. Langer, M. Sams, P. Heil, and H. Schulze Frequency and periodicity are represented in orthogonal maps in the human auditory cortex: evidence from magnetoencephalography J. Comp. Physiol. A: Neuroethol. 181 1997 665 676 (Pubitemid 28029273)
22. H.-T. Lin, C.-J. Lin, and R. Weng A note on Platt's probabilistic outputs for support vector machine Machine Learning 68 2007 267 276 (Pubitemid 47312490)
23. Luo, J., Caputo, B., Zweig, A., Bach, J.-H., Anemüller, J., 2008. Object category detection using audio-visual cues. In: Proc. ICVS (Santorini, Greece).
24. Maganti, H.K., Motlicek, P., Perez, D.G., 2007. Unsupervised speech/non-speech detection for automatic speech recognition in meeting rooms. In: Proc. ICASSP (Honolulu).
25. Markaki, M., Holzapfel, A., Stylianou, Y., 2008. Singing voice detection using modulation frequency features. In: Workshop on Statistical and Perceptual Audition (Brisbane), pp. 7-10.
26. Martin, A., Doddington, G., Kamm, T., Ordowski, M., Przybocki, M., 1997. The Det Curve in Assessment of Detection Task Performance. In: Proc. Eurospeech, pp. 1895-1898.
27. M. Marzinzik, and B. Kollmeier Speech pause detection for noise spectrum estimation by tracking power envelope dynamics IEEE Trans. Speech Audio Process. 10 2002 109 118 (Pubitemid 34295270)
28. N. Mesgarani, S.V. David, J.B. Fritz, and S.A. Shamma Phoneme representation and classification in primary auditory cortex J. Acoust. Soc. Amer. 123 2008 899 909 (Pubitemid 351208356)
29. 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. 14 2006 920 930 (Pubitemid 46547653)
30. Meyer, B.T., Kollmeier, B., 2008. Optimization and evaluation of Gabor feature sets for ASR. In: Proc. InterSpeech (Brisbane, Australia), pp. 906-909.
31. Ostendorf, M., Hohmann, V., Kollmeier, B., 1998. Classification of acoustical signals based on the analysis of modulation spectra for the application in digital hearing aids. In: Proc. DAGA (Zurich, Switzerland), pp. 402-403.
32. J. Platt Probabilistic Outputs for Support Vector Machines and Comparison to Regularized Likelihood Methods 2000 The MIT Press Cambridge, Massachusetts
33. L.R. Rabiner, and M.R. Sambur An algorithm for determining the endpoints of isolated utterances Bell Syst. Tech. J. 54 1975 297 315
34. C.E. Schreiner, and G. Langner Periodicity coding in the inferior colliculus of the cat. II. Topographical organization J. Neurophysiol. 60 1988 1823 1840 (Pubitemid 19017452)
35. Shire, M.L., Chen, B.Y., 2000. Data-driven RASTA Filters in Reverberation. In: ICASSP (Istanbul).
36. J. Tchorz, and B. Kollmeier SNR estimation based on amplitude modulation analysis with applications to noise suppression IEEE Trans. Speech Audio Process. 11 2003 184 192
37. V. Vapnik The Nature of Statistical Learning Theory 1995 Springer
38. Young, S., Evermann, G., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P., 2002. The HTK book (for HTK version 3.2) (Cambridge University, Eng. Dept.).
39. E. Zwicker Subdivision of the audible frequency range into critical bands J. Acoust. Soc. Amer. 33 1961 248