Automatic Speech Recognition and Speech Activity Detection in the CHIL Smart Room

  • Stephen M. Chu
  • Etienne Marcheret
  • Gerasimos Potamianos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3869)

Abstract

An important step to bring speech technologies into wide deployment as a functional component in man-machine interfaces is to free the users from close-talk or desktop microphones, and enable far-field operation in various natural communication environments. In this work, we consider far-field automatic speech recognition and speech activity detection in conference rooms. The experiments are conducted on the smart room platform provided by the CHIL project. The first half of the paper addresses the development of speech recognition systems for the seminar transcription task. In particular, we look into the effect of combining parallel recognizers in both single-channel and multi-channel settings. In the second half of the paper, we describe a novel algorithm for speech activity detection based on fusing phonetic likelihood scores and energy features. It is shown that the proposed technique is able to handle non-stationary noise events and achieves good performance on the CHIL seminar corpus.

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References

  1. 1.
    Brandstein, M., Ward, D. (eds.): Microphone Arrays. Springer, Berlin (2000)Google Scholar
  2. 2.
    Li, Q., Zheng, J., Zhou, Q., Lee, C.-H.: A robust, real-time end-point detector with energy normalization for ASR in adverse environments. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 233–236 (2001)Google Scholar
  3. 3.
    Martin, A., Charlet, D., Mauuary, L.: Robust speech/non-speech detection using LDA applied to MFCC. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 237–240 (2001)Google Scholar
  4. 4.
    Padrell, J., Macho, D., Nadeu, C.: Robust speech activity detection using LDA applied to FF parameters. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (2005)Google Scholar
  5. 5.
    Macho, D., et al.: Automatic speech activity detection, source localization, and speech recognition on the CHIL seminar corpus. In: IEEE International Conference on Multimedia & Expo., Amsterdam, Netherlands (2005)Google Scholar
  6. 6.
    Ramabhadran, B., Huang, J., Picheny, M.: Towards automatic transcription of large spoken archives – English ASR for the MALACH project. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (2003)Google Scholar
  7. 7.
    Povey, D., Kingsbury, B., Mangu, L., Saon, G., Soltau, H., Zweig, G.: fMPE: discriminatively trained features for speech recognition. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 961–964 (2005)Google Scholar
  8. 8.
    Fiscus, J.G.: A post-processing system to yield reduced word error rates: recognizer output voting error reduction (ROVER). In: Proceedings of the 1997 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 347–354 (1997)Google Scholar
  9. 9.
    Monkowski, M.: Automatic gain control in a speech recognition system. U.S. Patent 6, 314,396 (November 6, 2001) Google Scholar
  10. 10.
    Macho, D.: Speech activity detection: summary of CHIL evaluation run #1 (January 2005), http://chil.server.de/servlet/is/3870/

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stephen M. Chu
    • 1
  • Etienne Marcheret
    • 1
  • Gerasimos Potamianos
    • 1
  1. 1.Human Language Technologies, IBM T.J. Watson Research CenterYorktown HeightsUSA

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