Cauchi, B., Siedenburg, K., Santos, J.F. et al. (3 more authors) (2019) Non-intrusive speech quality prediction using modulation energies and LSTM-network. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27 (7). pp. 1151-1163. ISSN 2329-9290
Abstract
Many signal processing algorithms have been proposed to improve the quality of speech recorded in the presence of noise and reverberation. Perceptual measures, i.e., listening tests, are usually considered the most reliable way to evaluate the quality of speech processed by such algorithms but are costly and time-consuming. Consequently, speech enhancement algorithms are often evaluated using signal-based measures, which can be either intrusive or non-intrusive. As the computation of intrusive measures requires a reference signal, only non-intrusive measures can be used in applications for which the clean speech signal is not available. However, many existing non-intrusive measures correlate poorly with the perceived speech quality, particularly when applied over a wide range of algorithms or acoustic conditions. In this paper, we propose a novel non-intrusive measure of the quality of processed speech that combines modulation energy features and a recurrent neural network using long short-term memory cells. We collected a dataset of perceptually evaluated signals representing several acoustic conditions and algorithms and used this dataset to train and evaluate the proposed measure. Results show that the proposed measure yields higher correlation with perceptual speech quality than that of benchmark intrusive and non-intrusive measures when considering various categories of algorithms. Although the proposed measure is sensitive to mismatch between training and testing, results show that it is a useful approach to evaluate specific algorithms over a wide range of acoustic conditions and may, thus, become particularly useful for real-time selection of speech enhancement algorithm settings.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Speech quality; non-intrusive prediction; modulation energy; LSTM-network |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Apr 2020 09:58 |
Last Modified: | 18 May 2020 07:43 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/taslp.2019.2912123 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:159132 |