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

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Machine Learning for Multimodal Interaction (MLMI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3869))

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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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© 2006 Springer-Verlag Berlin Heidelberg

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Chu, S.M., Marcheret, E., Potamianos, G. (2006). Automatic Speech Recognition and Speech Activity Detection in the CHIL Smart Room. In: Renals, S., Bengio, S. (eds) Machine Learning for Multimodal Interaction. MLMI 2005. Lecture Notes in Computer Science, vol 3869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677482_29

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  • DOI: https://doi.org/10.1007/11677482_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32549-9

  • Online ISBN: 978-3-540-32550-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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