A metric for predicting binaural speech intelligibility in stationary noise and competing speech maskers

Tang, Y; Cooke, M; Fazenda, BM; Cox, TJ

Abstract

One criterion in the design of binaural sound scenes in audio production is the extent to which the intended speech message is correctly understood. Object-based audio broadcasting systems have permitted sound editors to gain more access to the metadata (e.g., intensity and location) of each sound source, providing better control over speech intelligibility. The current study describes and evaluates a binaural distortion-weighted glimpse proportion metric -- BiDWGP -- which is motivated by better-ear glimpsing and binaural masking level differences. BiDWGP predicts intelligibility from two alternative input forms: either binaural recordings or monophonic recordings from each sound source along with their locations. Two listening experiments were performed with stationary noise and competing speech, one in the presence of a single masker, the other with multiple maskers, for a variety of spatial configurations. Overall, BiDWGP with both input forms predicts listener keyword scores with correlations of 0.95 and 0.91 for single- and multi-masker conditions, respectively. When considering masker type separately, correlations rise to 0.95 and above for both types of maskers. Predictions using the two input forms are very similar, suggesting that BiDWGP can be applied to the design of sound scenes where only individual sound sources and their locations are available.

• Journal Article Type: Article
• Acceptance Date: Aug 25, 2016
• Online Publication Date: Sep 21, 2016
• Publication Date: Sep 21, 2016
• Deposit Date: Sep 13, 2016
• Publicly Available Date: Sep 22, 2016
• Journal: The Journal of the Acoustical Society of America (JASA)
• Print ISSN: 0001-4966
• Electronic ISSN: 1520-8524
• Volume: 140
• Issue: 3
• Pages: 1858-1870
• DOI: https://doi.org/10.1121/1.4962484
• Publisher URL: http://dx.doi.org/10.1121/1.4962484
• Related Public URLs: http://scitation.aip.org/content/asa/journal/jasa