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

Liu, Q, Yan, T, Jackson, P and Wang, W (2016) Predicting binaural speech intelligibility from signals estimated by a blind source separation algorithm In: INTERSPEECH 2016, 17th Annual Conference of the International Speech Communication Association, 2016-09-08 - 2016-09-12, San Francisco, US.

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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: Conference or Workshop Item (Conference Paper)
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Liu, QUNSPECIFIEDUNSPECIFIED
Yan, TUNSPECIFIEDUNSPECIFIED
Jackson, PUNSPECIFIEDUNSPECIFIED
Wang, WUNSPECIFIEDUNSPECIFIED
Date : 8 September 2016
Copyright Disclaimer : Copyright 2016 The Authors
Depositing User : Symplectic Elements
Date Deposited : 06 Sep 2016 13:22
Last Modified : 06 Sep 2016 13:22
URI: http://epubs.surrey.ac.uk/id/eprint/811761

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