Techniques for Robust Speech Recognition in Noisy and Reverberant Conditions

  • Guy J. Brown
  • Kalle J. Palomäki


Speech Recognition Automatic Speech Recognition Interaural Time Difference Automatic Speech Recognition System Reverberation Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Assmann, P. and Summerfield, Q., 2003, The perception of speech under adverse acoustic conditions, in: Speech processing in the auditory system (Springer handbook of auditory research vol. 18), Greenberg, S., Ainsworth, W., eds. Springer-Verlag.Google Scholar
  2. Barker, J., Cooke, M.P., and Green, P.D., 2001, Robust ASR based on clean speech models: An evaluation of missing data techniques for connected digit recognition in noise. Proc. EUROSPEECH, 2001, pp. 213–217.Google Scholar
  3. Bregman, A.S., 1990, Auditory Scene Analysis. MIT Press, Cambridge, MA.Google Scholar
  4. Brown, G. J., Barker, J., and Wang, D. L., 2001, A neural oscillator sound separator for missing data speech recognition. Proc. IJCNN 2001, pp. 2907–2912.Google Scholar
  5. Cooke, M.P., Green, P.D., Josifovski, L., and Vizinho, A., 2001, Robust automatic speech recognition with missing and unreliable acoustic data. Speech Comm., 34, pp. 267–285.Google Scholar
  6. Culling, J.F. and Summerfield, Q., 1995, Perceptual separation of concurrent speech sounds: Absence of across-frequency grouping by common interaural delay. J. Acoust. Soc. Am., 98(2), pp. 785–797.Google Scholar
  7. Darwin, C.J. and Hukin, R.W., 2000, Effects of reverberation on spatial, prosodic and vocaltract size cues to selective attention. J. Acoust. Soc. Am., 108(1), pp. 335–342.CrossRefGoogle Scholar
  8. Hermansky, H., 1998, Should recognisers have ears? Speech Comm., 25, pp. 3–27.Google Scholar
  9. Hukin, R.W. and Darwin, C.J., 1995, Effects of contralateral presentation and of interaural time differences in segregating a harmonic from a vowel. J. Acoust. Soc. Am., 98(3), pp. 1380–1387.CrossRefGoogle Scholar
  10. Kingsbury, B.E.D., 1998, Perceptually inspired signal-processing strategies for robust speech recognition in reverberant environments. PhD thesis, Univ. California, Berkeley.Google Scholar
  11. Lippmann, R.P., 1997, Speech recognition by machines and humans. Speech Comm., 22, pp. 1–15.Google Scholar
  12. Litovsky, R.Y., Colburn, S.H., Yost, W.A., and Guzman, S.J., 1999, The precedence effect. J. Acoust. Soc. Am., 106(4), pp. 1633–1654.CrossRefGoogle Scholar
  13. Palomäki, K.J., Brown, G.J., and Barker, J., 2002, Missing data speech recognition in reverberant conditions. Proc. ICASSP, Orlando, 13th–17th May, pp. 65–68.Google Scholar
  14. Palomäki, K.J., Brown, G.J., and Wang, D.L., 2004a, A binaural processor for missing data speech recognition in the presence of noise and small-room reverberation. Speech Comm., in press.Google Scholar
  15. Palomäki, K.J., Brown, G.J., and Barker, J., 2004b, Techniques for handling convolutional distortion with ‘missing data’ automatic speech recognition. Speech Comm., in press.Google Scholar
  16. Shamsoddini, A. and Denbigh, P.N., 2001, A sound segregation algorithm for reverberant conditions. Speech Comm., 33, pp. 179–196.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Guy J. Brown
    • 1
  • Kalle J. Palomäki
    • 2
  1. 1.Department of Computer ScienceUniversity of SheffieldUK
  2. 2.Laboratory of Acoustics and Audio Signal ProcessingHelsinki University of TechnologyFinland

Personalised recommendations