Convolutional Recurrent Smart Speech Enhancement Architecture for Hearing Aids

Conference paper

Abdallah Abdelhafiz Nossier, S., Wall, J., Moniri, M., Glackin, C. and Cannings, N. 2022. Convolutional Recurrent Smart Speech Enhancement Architecture for Hearing Aids. INTERSPEECH 2022. Incheon, Korea 18 - 22 Sep 2022
AuthorsAbdallah Abdelhafiz Nossier, S., Wall, J., Moniri, M., Glackin, C. and Cannings, N.
TypeConference paper
Abstract

Speech enhancement is the process of removing noise to improve speech quality and intelligibility for applications including hearing aids. Many deep neural networks for speech enhancement have shown great ability in eliminating noise, regardless of its type. In hearing aids, this process may result in removing important noise used in emergency situations, such as fire alarms and car horns. In order to prevent this, a smart speech enhancement architecture is presented in this paper, where a convolution-based noise classifier is used to detect emergency noise and activates the speech enhancement model to run in an audio enhancement mode, in which both the emergency noise and the speech are the target system output. The developed speech enhancement model is a deep convolutional recurrent network with several dilated layers to improve feature extraction without increasing network complexity. The results show that the speech enhancement model outperforms state of the art
architectures by a 0.22 increase in the PESQ score. Moreover,
the smart speech enhancement architecture improves speech
and emergency noise quality when evaluated using objective
metrics for both normal and hearing-impaired listeners.

KeywordsConvolutional recurrent network; deep learning; hearing aids; noise classification; speech enhancement
Year2022
ConferenceINTERSPEECH 2022
Accepted author manuscript
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Publication dates
Print17 Nov 2022
Publication process dates
Accepted14 Jun 2022
Deposited04 Jul 2022
Web address (URL) of conference proceedingshttps://www.interspeech2022.org/program/
Copyright holder© 2022, The Authors
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