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A NEW MASK-BASED OBJECTIVE MEASURE FOR PREDICTING THE INTELLIGIBILITY OF BINARY MASKED SPEECH.

Chengzhu Yu1, Kamil K Wójcicki, P C Loizou

  • 1Dept. of Electrical Engineering, Erik Jonsson School of Enigneering and Computer Science, University of Texas at Dallas, Richardson, TX 75080.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
|November 7, 2013
PubMed
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This study introduces a novel weighting method for objective speech intelligibility measures. By considering loudness in time-frequency units, the new measure significantly improves performance over existing methods.

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Speech intelligibilityideal binary maskobjective measurespeech separation

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Area of Science:

  • Speech processing
  • Acoustic signal analysis
  • Machine learning for audio

Background:

  • Objective speech intelligibility measures evaluate binary masking algorithms.
  • Current measures compare estimated masks to ideal binary masks (IdBM).
  • Existing methods often weigh all time-frequency (T-F) units equally.

Purpose of the Study:

  • To enhance existing mask-based objective speech intelligibility measures.
  • To introduce a T-F unit weighting strategy based on target or masker loudness.

Main Methods:

  • Developed a novel objective measure for speech intelligibility.
  • Weighted time-frequency units based on loudness of target speech or masker.
  • Compared the proposed measure against existing mask-based objective measures.

Main Results:

  • The proposed objective measure demonstrated significantly improved performance.
  • Weighting T-F units by loudness enhanced measure accuracy.
  • Outperformed two existing mask-based objective measures.

Conclusions:

  • Loudness-weighted T-F units improve objective speech intelligibility measures.
  • The proposed method offers a more effective evaluation of masking algorithms.
  • This approach advances the field of speech signal processing.