Improving intelligibility prediction under informational masking using an auditory saliency model

Yan Tang, Trevor J. Cox

Research output: Contribution to journalConference articlepeer-review


The reduction of speech intelligibility in noise is usually dominated by energetic masking (EM) and informational masking (IM). Most state-of-the-art objective intelligibility measures (OIM) estimate intelligibility by quantifying EM. Few measures model the effect of IM in detail. In this study, an auditory saliency model, which intends to measure the probability of the sources obtaining auditory attention in a bottom-up process, was integrated into an OIM for improving the performance of intelligibility prediction under IM. While EM is accounted for by the original OIM, IM is assumed to arise from the listener’s attention switching between the target and competing sounds existing in the auditory scene. The performance of the proposed method was evaluated along with three reference OIMs by comparing the model predictions to the listener word recognition rates, for different noise maskers, some of which introduce IM. The results shows that the predictive accuracy of the proposed method is as good as the best reported in the literature. The proposed method, however, provides a physiologically-plausible possibility for both IM and EM modelling.

Original languageEnglish (US)
Pages (from-to)113-119
Number of pages7
JournalProceedings of the 21st International Conference on Digital Audio Effects (DAFx-18)
StatePublished - 2018
Externally publishedYes
Event21st International Conference on Digital Audio Effects, DAFx 2018 - Aveiro, Portugal
Duration: Sep 4 2018Sep 8 2018

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Computer Science Applications
  • Signal Processing
  • Music


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