Tu, Z., Ma, N. orcid.org/0000-0002-4112-3109 and Barker, J. orcid.org/0000-0002-1684-5660 (2021) Optimising hearing aid fittings for speech in noise with a differentiable hearing loss model. In: Heřmanský, H., Černocký, H., Burget, L., Lamel, L., Scharenborg, O. and Motlicek, P., (eds.) Interspeech 2021. Interspeech 2021, 30 Aug - 03 Sep 2021, Brno, Czechia. ISCA - International Speech Communication Association , pp. 691-695. ISBN 9781713836902
Abstract
Current hearing aids normally provide amplification based on a general prescriptive fitting, and the benefits provided by the hearing aids vary among different listening environments despite the inclusion of noise suppression feature. Motivated by this fact, this paper proposes a data-driven machine learning technique to develop hearing aid fittings that are customised to speech in different noisy environments. A differentiable hearing loss model is proposed and used to optimise fittings with back-propagation. The customisation is reflected on the data of speech in different noise with also the consideration of noise suppression. The objective evaluation shows the advantages of optimised custom fittings over general prescriptive fittings.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. This is an author-produced version of a paper subsequently accepted at Interspeech 2021. For reuse permissions please contact the authors. |
Keywords: | Hearing aid speech processing; speech in noise; differentiable hearing loss model |
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) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/S031448/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Dec 2021 07:48 |
Last Modified: | 02 Dec 2021 07:48 |
Status: | Published |
Publisher: | ISCA - International Speech Communication Association |
Refereed: | Yes |
Identification Number: | 10.21437/interspeech.2021-1613 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:181101 |