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Discriminative Beamforming with Phase-Aware Neural Networks for Speech Enhancement and Recognition

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Abstract

Speech-processing systems such as automatic speech recognition (ASR) usually consist of a large number of steps to accomplish their tasks. Due to the long processing pipeline, the processing steps are usually designed to optimize cost functions that are not directly related to the task, leading to suboptimal performance. In this chapter, we introduce a beamforming (BF) network to perform spatial filtering that is optimal for the ASR task. The BF network takes in array signals and predicts the optimal beamforming parameters in the frequency domain, assuming that the array geometry does not change. The network consists of both deterministic processing steps and trainable steps realized by neural networks and trained to minimize the cross-entropy cost function of ASR. In our experiments, the BF network is trained with both artificially generated and real microphone array signals. On the AMI meeting transcription, we found that the trained BF network produces competitive ASR results compared to traditional delay-and-sum beamforming on unseen array signals.

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Author information

Authors and Affiliations

  1. Temasek Laboratories, Nanyang Technological University, 9th Storey, BorderX Block, Research Techno Plaza, 50 Nanyang Drive, Singapore, 637553, Singapore

    Xiong Xiao

  2. Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA

    Shinji Watanabe & John R. Hershey

  3. Microsoft Research, City Center Square, Bellevue, WA, 98004, USA

    Hakan Erdogan & Michael L. Seltzer

  4. Brooklyn College (CUNY), 2900 Bedford Ave, Brooklyn, NY, 11210, USA

    Michael Mandel

  5. Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave., Chicago, IL, 60637, USA

    Liang Lu

  6. Johns Hopkins University, Baltimore, MD, USA

    Guoguo Chen

  7. Massachusetts Institute of Technology, Cambridge, MA, USA

    Yu Zhang

  8. Tencent AI Lab, 10900 NE 8th Street, Bellevue, WA, 98004, USA

    Dong Yu

Authors
  1. Xiong Xiao
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  2. Shinji Watanabe
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  3. Hakan Erdogan
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  4. Michael Mandel
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  5. Liang Lu
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  6. John R. Hershey
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  7. Michael L. Seltzer
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  8. Guoguo Chen
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  9. Yu Zhang
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  10. Dong Yu
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Corresponding author

Correspondence to Xiong Xiao .

Editor information

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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Xiao, X. et al. (2017). Discriminative Beamforming with Phase-Aware Neural Networks for Speech Enhancement and Recognition. 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_4

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  • DOI: https://doi.org/10.1007/978-3-319-64680-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64679-4

  • Online ISBN: 978-3-319-64680-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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