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Modelling speech intelligibility in adverse conditions.

Søren Jørgensen1, Torsten Dau

  • 1Department of Electrical Engineering, Technical University of Denmark, Lyngby, Denmark.

Advances in Experimental Medicine and Biology
|May 30, 2013
PubMed
Summary
This summary is machine-generated.

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The speech-based envelope power spectrum model (sEPSM) uses signal-to-noise ratio in the envelope domain (SNRenv) to predict speech intelligibility. While effective for noise and reverberation, it struggles with phase jitter, unlike the spectro-temporal modulation index (STMI).

Area of Science:

  • Acoustics and Auditory Perception
  • Speech Processing and Intelligibility

Background:

  • Classical speech intelligibility models like STI and SII have limitations with nonlinearly processed speech.
  • The speech-based envelope power spectrum model (sEPSM) was developed to address these limitations, focusing on envelope domain metrics.
  • Previous work highlighted the effectiveness of the signal-to-noise ratio in the envelope domain (SNRenv) for predicting intelligibility in noise and reverberation.

Purpose of the Study:

  • To further validate the SNRenv metric within the sEPSM framework.
  • To investigate the performance of sEPSM and the spectro-temporal modulation index (STMI) under various speech distortion conditions, including phase jitter.
  • To explore the necessity of across-frequency analysis for speech intelligibility modeling.

Main Methods:

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  • Evaluated a short-term version of the sEPSM to predict speech masking release with different speech materials and modulated interferers.
  • Compared the predictive capabilities of sEPSM (using SNRenv) and STMI (using STI's decision metric) against speech distortions like spectral subtraction and phase jitter.
  • Analyzed the role of envelope-based metrics versus spectral structure analysis in intelligibility prediction.

Main Results:

  • The SNRenv metric, as used in sEPSM, successfully predicted speech intelligibility for reverberant and spectrally subtracted speech.
  • A short-term sEPSM variant demonstrated predictive power for speech masking release.
  • sEPSM failed to account for intelligibility degradation caused by phase jitter, a distortion affecting spectral structure but not the broadband temporal envelope.

Conclusions:

  • The SNRenv metric is a powerful tool for predicting speech intelligibility, particularly in noisy and reverberant conditions.
  • While sEPSM excels with envelope-based distortions, it cannot model the effects of phase jitter.
  • Explicit across-frequency analysis, as potentially captured by STMI, is crucial for intelligibility prediction in certain distortion types, though its auditory implementation remains unclear.