Improving Dysarthric Speech Intelligibility using Cycle-consistent Adversarial Training
Yang SH, Chung M (2020)
Publication Type: Conference contribution
Publication year: 2020
Publisher: SciTePress
Pages Range: 308-313
Conference Proceedings Title: BIOSIGNALS 2020 - 13th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020
Event location: Valletta
ISBN: 9789897583988
Abstract
Dysarthria is a motor speech impairment affecting millions of people. Dysarthric speech can be far less intelligible than those of non-dysarthric speakers, causing significant communication difficulties. The goal of our work is to develop a model for dysarthric to healthy speech conversion using Cycle-consistent GAN. Using 18,700 dysarthric and 8,610 healthy Korean utterances that were recorded for the purpose of automatic recognition of voice keyboard in a previous study, the generator is trained to transform dysarthric to healthy speech in the spectral domain, which is then converted back to speech. Objective evaluation using automatic speech recognition of the generated utterance on a held-out test set shows that the recognition performance is improved compared with the original dysarthic speech after performing adversarial training, as the absolute SER has been lowered by 33.4%. It demonstrates that the proposed GAN-based conversion method is useful for improving dysarthric speech intelligibility.
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Korea, Republic of (KR)How to cite
APA:
Yang, S.H., & Chung, M. (2020). Improving Dysarthric Speech Intelligibility using Cycle-consistent Adversarial Training. In Pedro Gomez Vilda, Ana Fred, Hugo Gamboa (Eds.), BIOSIGNALS 2020 - 13th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 (pp. 308-313). Valletta, MT: SciTePress.
MLA:
Yang, Seung Hee, and Minhwa Chung. "Improving Dysarthric Speech Intelligibility using Cycle-consistent Adversarial Training." Proceedings of the 13th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2020 - Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020, Valletta Ed. Pedro Gomez Vilda, Ana Fred, Hugo Gamboa, SciTePress, 2020. 308-313.
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