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Relative Transfer Function Estimation from Speech Keywords

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Latent Variable Analysis and Signal Separation (LVA/ICA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10891))

Abstract

Far-field speech capture systems rely on microphone arrays to spatially filter sound, attenuating unwanted interference and noise and enhancing a speech signal of interest. To design effective spatial filters, we must first estimate the acoustic transfer functions between the source and the microphones. It is difficult to estimate these transfer functions if the source signals are unknown. However, in systems that are activated by a particular speech phrase, we can use that phrase as a pilot signal to estimate the relative transfer functions. Here, we propose a method to estimate relative transfer functions from known speech phrases in the presence of background noise and interference using template matching and time-frequency masking. We find that the proposed method can outperform conventional estimation techniques, but its performance depends on the characteristics of the speech phrase.

Parts of this research were completed at Arm Research. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant Number DGE-1144245.

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Authors and Affiliations

  1. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Ryan M. Corey & Andrew C. Singer

Authors
  1. Ryan M. Corey
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  2. Andrew C. Singer
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Corresponding author

Correspondence to Ryan M. Corey .

Editor information

Editors and Affiliations

  1. Paul Sabatier University, Toulouse, France

    Yannick Deville

  2. Bar-Ilan University, Ramat Gan, Israel

    Sharon Gannot

  3. University of Surrey, Guildford, United Kingdom

    Russell Mason

  4. University of Surrey, Guildford, United Kingdom

    Mark D. Plumbley

  5. University of Surrey, Guildford, United Kingdom

    Dominic Ward

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Corey, R.M., Singer, A.C. (2018). Relative Transfer Function Estimation from Speech Keywords. In: Deville, Y., Gannot, S., Mason, R., Plumbley, M., Ward, D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science(), vol 10891. Springer, Cham. https://doi.org/10.1007/978-3-319-93764-9_23

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  • DOI: https://doi.org/10.1007/978-3-319-93764-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93763-2

  • Online ISBN: 978-3-319-93764-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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