A Deep Learning Speech Enhancement Architecture Optimised for Speech Recognition and Hearing Aids

Conference paper

Nossier, S. A., Wall, J., Moniri, M., Glackin, C. and Cannings, N. 2023. A Deep Learning Speech Enhancement Architecture Optimised for Speech Recognition and Hearing Aids. The 35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). Atlanta, Georgia (USA) 06 - 08 Nov 2023 IEEE Computer Society. https://doi.org/10.1109/ICTAI59109.2023.00088
AuthorsNossier, S. A., Wall, J., Moniri, M., Glackin, C. and Cannings, N.
TypeConference paper
Abstract

With the fast progression of the speech enhancement field after the introduction of deep learning techniques, there is a need to consider the adjustments needed to employ these techniques for real-life applications. In this work, we present an optimised deep learning speech enhancement architecture for automatic speech recognition and hearing aids, two key speech enhancement applications. A speech enhancement architecture with a signal-to-noise ratio switch is presented for automatic speech recognition systems, to avoid denoising artifacts that cause performance degradation in the case of clean or high signal-tonoise speech. Moreover, a smart speech enhancement architecture is presented for hearing aids to retain important emergency noise in the audio signal. The presented work achieved 13.9% reduction in the word error rate of an automatic speech recognition system. Additionally, the smart speech enhancement architecture resulted in 0.18 improvement in HAAQI audio quality metric.

KeywordsAutomatic speech recognition; convolutional classifiers; deep learning; hearing aids; speech enhancement
Year2023
ConferenceThe 35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
PublisherIEEE Computer Society
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Print2023
Publication process dates
Accepted04 Sep 2023
Deposited18 Sep 2023
Journal citationpp. 553-558
ISSN2375-0197
Book title2023 IEEE 35th International Conference on Tools with Artificial Intelligence Proceedings
ISBN9798350342734
Digital Object Identifier (DOI)https://doi.org/10.1109/ICTAI59109.2023.00088
Web address (URL) of conference proceedingshttps://www.computer.org/csdl/proceedings/ictai/2023/1T3d5DsZCfe
Copyright holder© 2023, IEEE.
Copyright informationPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
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