CUED Publications database

A data-driven non-intrusive measure of speech quality and intelligibility

Sharma, D and Wang, Y and Naylor, PA and Brookes, M (2016) A data-driven non-intrusive measure of speech quality and intelligibility. Speech Communication, 80. pp. 84-94. ISSN 0167-6393

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© 2016 Elsevier B.V. Speech signals are often affected by additive noise and distortion which can degrade the perceived quality and intelligibility of the signal. We present a new measure, NISA, for estimating the quality and intelligibility of speech degraded by additive noise and distortions associated with telecommunications networks, based on a data driven framework of feature extraction and tree based regression. The new measure is non-intrusive, operating on the degraded signal alone without the need for a reference signal. This makes the measure applicable to practical speech processing applications operating in the single-ended mode. The new measure has been evaluated against the intrusive measures PESQ and STOI. The results indicate that the accuracy of the new non-intrusive method is around 90% of the accuracy of the intrusive measures, depending on the test scenario. The NISA measure therefore provides non-intrusive (single-ended) PESQ and STOI estimates with high accuracy.

Item Type: Article
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:07
Last Modified: 23 Nov 2017 04:22