Skip to the content

Predicting binaural speech intelligibility from signals estimated by a blind source separation algorithm

Liu, Q, Tang, Y, Jackson, PJB and Wang, W 2016, 'Predicting binaural speech intelligibility from signals estimated by a blind source separation algorithm' , Proceedings of the Annual Conference of the International Speech Communication Association (Vol. 08-12-September-2016) , pp. 140-144.

[img]
Preview
PDF - Published Version
Download (485kB) | Preview

Abstract

State-of-the-art binaural objective intelligibility measures (OIMs) require individual source signals for making intelligibility predictions, limiting their usability in real-time online operations. This limitation may be addressed by a blind source separation (BSS) process, which is able to extract the underlying sources from a mixture. In this study, a speech source is presented with either a stationary noise masker or a fluctuating noise masker whose azimuth varies in a horizontal plane, at two speech-to-noise ratios (SNRs). Three binaural OIMs are used to predict speech intelligibility from the signals separated by a BSS algorithm. The model predictions are compared with listeners' word identification rate in a perceptual listening experiment. The results suggest that with SNR compensation to the BSS-separated speech signal, the OIMs can maintain their predictive power for individual maskers compared to their performance measured from the direct signals. It also reveals that the errors in SNR between the estimated signals are not the only factors that decrease the predictive accuracy of the OIMs with the separated signals. Artefacts or distortions on the estimated signals caused by the BSS algorithm may also be concerns.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Proceedings of the Annual Conference of the International Speech Communication Association (Vol. 08-12-September-2016)
Publisher: International Speech and Communication Association
ISSN: 2308-457X
Related URLs:
Funders: Engineering and Physical Sciences Research Council (EPSRC)
Depositing User: Y Tang
Date Deposited: 09 Sep 2016 08:00
Last Modified: 07 Dec 2016 10:43
URI: http://usir.salford.ac.uk/id/eprint/40055

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)

Downloads

Downloads per month over past year