Jasper Kirton-Wingate J.Kirton-wingate@napier.ac.uk
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Towards Individualised Speech Enhancement: An SNR Preference Learning System for Multi-Modal Hearing Aids
Kirton-Wingate, Jasper; Ahmed, Shafique; Gogate, Mandar; Tsao, Yu; Hussain, Amir
Authors
Shafique Ahmed
Dr. Mandar Gogate M.Gogate@napier.ac.uk
Senior Research Fellow
Yu Tsao
Prof Amir Hussain A.Hussain@napier.ac.uk
Professor
Contributors
Dr Kia Dashtipour K.Dashtipour@napier.ac.uk
Editor
Abstract
Since the advent of deep learning (DL), speech enhancement (SE) models have performed well under a variety of noise conditions. However, such systems may still introduce sonic artefacts, sound unnatural, and restrict the ability for a user to hear ambient sound which may be of importance. Hearing Aid (HA) users may wish to customise their SE systems to suit their personal preferences and day-to-day lifestyle. In this paper, we introduce a preference learning based SE (PLSE) model for future multi-modal HAs that can contextually exploit audio and visual information to improve listening comfort (LC). The proposed system estimates the Signal-to-noise ratio (SNR) as a basic objective speech quality measure which quantifies the relative amount of background noise present in speech, and directly correlates to the intelligibility of the signal. This is used alongside a preference elicitation framework which learns a predictive function to determine the target SNR. The system is novel, scaling the output of an AudioVisual (AV) DL-based SE model to provide HA users with individualised SE. Preliminary results support the hypothesis of improving the overall subjective LC, without significantly impeding the speech intelligibility.
Citation
Kirton-Wingate, J., Ahmed, S., Gogate, M., Tsao, Y., & Hussain, A. (2023). Towards Individualised Speech Enhancement: An SNR Preference Learning System for Multi-Modal Hearing Aids. In K. Dashtipour (Ed.), Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). https://doi.org/10.1109/icasspw59220.2023.10193122
Conference Name | 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) |
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Conference Location | Rhodes Island, Greece |
Start Date | Jun 4, 2023 |
End Date | Jun 10, 2023 |
Acceptance Date | Apr 15, 2023 |
Online Publication Date | Jun 4, 2023 |
Publication Date | 2023 |
Deposit Date | Jan 22, 2024 |
Publisher | Institute of Electrical and Electronics Engineers |
Book Title | Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) |
ISBN | 9798350302622 |
DOI | https://doi.org/10.1109/icasspw59220.2023.10193122 |
Public URL | http://researchrepository.napier.ac.uk/Output/3489689 |
Publisher URL | https://2023.ieeeicassp.org/ |
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