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Using statistical decision theory to predict speech intelligibility. I. Model structure.

H Müsch1, S Buus

  • 1Communications and Digital Signal Processing Center, Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, USA.

The Journal of the Acoustical Society of America
|June 27, 2001
PubMed
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This study presents a new speech recognition sensitivity (SRS) model to predict speech intelligibility. The SRS model accounts for spectral band interactions and linguistic factors, offering a more comprehensive approach to understanding speech recognition performance.

Area of Science:

  • Auditory perception
  • Speech processing
  • Psychoacoustics

Background:

  • Accurate prediction of speech intelligibility is crucial for audiological applications and assistive listening device development.
  • Existing models like the articulation index have limitations in accounting for complex auditory processing and linguistic influences.

Purpose of the Study:

  • To introduce and validate a novel model, the speech recognition sensitivity (SRS) model, for predicting speech intelligibility.
  • To incorporate statistical decision theory to enhance the prediction of speech recognition performance.

Main Methods:

  • The SRS model utilizes long-term average speech spectrum, masking excitation, linguistic entropy, and number of response alternatives.
  • It accounts for synergetic and redundant interactions among spectral bands, differentiating it from traditional models.

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Main Results:

  • The SRS model demonstrates the ability to predict speech recognition performance based on acoustic and linguistic parameters.
  • It successfully models the influence of linguistic entropy on listener identification sensitivity and accounts for the impact of response alternatives.

Conclusions:

  • The SRS model provides a robust framework for predicting speech intelligibility, outperforming existing models by considering spectral interactions.
  • It accurately predicts the differential effects of linguistic entropy and its interactions with signal-to-noise ratio and language proficiency.