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Distant Speech Recognition Experiments Using the AMI Corpus

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New Era for Robust Speech Recognition

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Abstract

This chapter reviews distant speech recognition experimentation using the AMI corpus of multiparty meetings. The chapter compares conventional approaches using microphone array beamforming followed by single-channel acoustic modelling with approaches which combine multichannel signal processing with acoustic modelling in the context of convolutional networks.

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Notes

  1. 1.

    Mics 1 and 5 were used in the 2-mic case; mics 1, 3, 5 and 7 in the 4-mic case.

  2. 2.

    However, since the networks were being tasked with additional processing, it may be that deeper architectures would be more suitable.

  3. 3.

    The convolution of two vectors of size X and Y may result either in a vector of size X + Y − 1 for a full convolution with zero-padding of non-overlapping regions, or a vector of size X − Y + 1 for a valid convolution where only the points which overlap completely are considered.

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Authors and Affiliations

  1. Centre for Speech Technology Research, University of Edinburgh, Edinburgh, UK

    Steve Renals & Pawel Swietojanski

Authors
  1. Steve Renals
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  2. Pawel Swietojanski
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Correspondence to Steve Renals .

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Editors and Affiliations

  1. Mitsubishi Electric Research Laboratories (MERL), Cambridge, Massachusetts, USA

    Shinji Watanabe

  2. NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan

    Marc Delcroix

  3. Language Technologies Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Florian Metze

  4. Mitsubishi Electric Research Laboratories (MERL), Cambridge, Massachusetts, USA

    John R. Hershey

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Renals, S., Swietojanski, P. (2017). Distant Speech Recognition Experiments Using the AMI Corpus. In: Watanabe, S., Delcroix, M., Metze, F., Hershey, J. (eds) New Era for Robust Speech Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-64680-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-64680-0_16

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  • Online ISBN: 978-3-319-64680-0

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