Predicting the intelligibility of noise-corrupted speech non-intrusively by across-band envelope correlation. (February 2016)

• Record Type: Journal Article
• Authors: Chen, Fei
• Is Part Of: Biomedical signal processing and control. Volume 24:(2015)
• Page Start: 109
• Page End: 113
• Publication Date: 2016-02
• Subjects: Speech perception in noise -- Intelligibility prediction -- Across-band envelope correlation; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.09.007
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: ABECm may efficiently and non-intrusively predict speech intelligibility in noise. High correlation was obtained between ABECm values and intelligibility scores. The computation of ABECm does not need access to the clean reference signal. Abstract: Recent psychoacoustic studies found that across-band envelope correlation (ABEC) carried important information for speech intelligibility. This motivated the present work to propose an ABEC-based intelligibility measure that could be used to non-intrusively predict speech intelligibility in noise using only temporal envelope waveforms extracted from the noise-corrupted speech. The proposed ABEC-based metric (denoted as ABECm) was computed by averaging the correlation coefficients of mean-removed envelope waveforms from adjacent frequency bands of the noise-corrupted speech. The ABECm measures were evaluated with intelligibility scores obtained from normal-hearing listeners presented with sentences corrupted by four types of maskers in a total of 22 conditions. High correlation ( r = 0.96) was obtained between ABECm values and listeners' sentence recognition scores, and this correlation was comparable to those computed with existing intrusive and non-intrusive intelligibility measures. This suggests that across-band envelope correlation may work as a simple but efficient predictor of speech intelligibility in noise, whose computation does not need access to the clean reference signal.