A non-intrusive method for estimating binaural speech intelligibility from noise-corrupted signals captured by a pair of microphones

Yan Tang, Qingju Liu, Wenwu Wang, Trevor J. Cox

Research output: Contribution to journalArticle

Abstract

A non-intrusive method is introduced to predict binaural speech intelligibility in noise directly from signals captured using a pair of microphones. The approach combines signal processing techniques in blind source separation and localisation, with an intrusive objective intelligibility measure (OIM). Therefore, unlike classic intrusive OIMs, this method does not require a clean reference speech signal and knowing the location of the sources to operate. The proposed approach is able to estimate intelligibility in stationary and fluctuating noises, when the noise masker is presented as a point or diffused source, and is spatially separated from the target speech source on a horizontal plane. The performance of the proposed method was evaluated in two rooms. When predicting subjective intelligibility measured as word recognition rate, this method showed reasonable predictive accuracy with correlation coefficients above 0.82, which is comparable to that of a reference intrusive OIM in most of the conditions. The proposed approach offers a solution for fast binaural intelligibility prediction, and therefore has practical potential to be deployed in situations where on-site speech intelligibility is a concern.

Original languageEnglish (US)
Pages (from-to)116-128
Number of pages13
JournalSpeech Communication
Volume96
DOIs
StatePublished - Feb 2018
Externally publishedYes

Keywords

  • Binaural intelligibility
  • Blind source localisation
  • Blind source separation
  • Glimpsing
  • Microphone
  • Neural network
  • Noise
  • Non-intrusive
  • Objective intelligibility measure

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Communication
  • Language and Linguistics
  • Linguistics and Language
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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