Enhancing Automatic Speech Recognition Quality with a Second-Stage Speech Enhancement Generative Adversarial Network

Conference paper

Nossier, S. A., Wall, J., Moniri, M., Glackin, C. and Cannings, N. 2023. Enhancing Automatic Speech Recognition Quality with a Second-Stage Speech Enhancement Generative Adversarial Network. 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/ICTAI59109.2023.00087
AuthorsNossier, S. A., Wall, J., Moniri, M., Glackin, C. and Cannings, N.
TypeConference paper
Abstract

Speech enhancement is an essential preprocessing stage for automatic speech recognition in noisy conditions; however, the distortion caused by the denoising process may lead to degradation in automatic speech recognition performance. This paper presents a deep learning-based speech enhancement architecture to overcome this issue by applying a second stage network that deals with distortion noise. Moreover, a signal-to-noise ratio binary classifier is implemented to activate the speech enhancement network for intrusive noise environments only, which improves the overall performance. The proposed architecture outperforms powerful models in the literature, as it improves a challenging noisy speech test set by 0.8 and 5.9% improvement in the quality and intelligibility scores, respectively. Furthermore, the architecture improves the performance of automatic speech recognition with a 13.8% reduction in the word error rate at 0 dB signal-to-noise ratio. Finally, the second-stage network was proven to improve the performance of first-stage speech enhancement models, not previously seen in the training process.

Keywordsautomatic speech recognition; deep learning; generative adversarial network; speech distortion; speech enhancement
Year2023
ConferenceThe 35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
PublisherIEEE Computer Society
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Print2023
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Accepted04 Sep 2023
Deposited18 Sep 2023
Journal citationpp. 546-552
ISSN2375-0197
Book title2023 IEEE 35th International Conference on Tools with Artificial Intelligence Proceedings
ISBN9798350342734
Digital Object Identifier (DOI)https://doi.org/10.1109/ICTAI59109.2023.00087
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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