Data Augmentation Technology for Dysarthria Assistive Systems

Wei Chung Chu; Ying Hsiu Hung; Wei Zhong Zheng; Ying Hui Lai

Data Augmentation Technology for Dysarthria Assistive Systems

Wei Chung Chu, Ying Hsiu Hung, Wei Zhong Zheng, Ying Hui Lai

Department of Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Voice-driven communication aids are one of the methods commonly used by patients with dysarthria. However, this type of assistive devices demands a large amount of voice data from patients to increase the effectiveness. In the meantime, this will sink patients into an overwhelming recording burden. Due to those difficulties, this research proposes a voice augmentation system to conquer the aforementioned concern. Furthermore, the system can improve the recognition efficiency. The results of this research reveal that the proposed speech generator system for dysarthria can launch corpus to be more similarities to the patient's speech. Moreover, the recognition rate, in duplicate sentences, has been improved and promoted to the higher level. The word error rate can be reduced from 64.42% to 4.39% in the case of patients with Free-talk. According to these results, our proposed system can provide more reliable and helpful technique for the development of communication aids.

Original language	English
Title of host publication	ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing
Editors	Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
Publisher	The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Pages	144-150
Number of pages	7
ISBN (Electronic)	9789869576949
State	Published - 2021
Event	33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 - Taoyuan, Taiwan Duration: 15 Oct 2021 → 16 Oct 2021

Publication series

Name	ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing

Conference

Conference	33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021
Country/Territory	Taiwan
City	Taoyuan
Period	15/10/21 → 16/10/21

Keywords

Communication assistance system
Data augmentation
Deep learning
Dysarthria

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

Chu, W. C., Hung, Y. H., Zheng, W. Z., & Lai, Y. H. (2021). Data Augmentation Technology for Dysarthria Assistive Systems. In L.-H. Lee, C.-H. Chang, & K.-Y. Chen (Eds.), ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (pp. 144-150). (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing). The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).

Chu, Wei Chung ; Hung, Ying Hsiu ; Zheng, Wei Zhong et al. / Data Augmentation Technology for Dysarthria Assistive Systems. ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. editor / Lung-Hao Lee ; Chia-Hui Chang ; Kuan-Yu Chen. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 2021. pp. 144-150 (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing).

@inproceedings{f5cc7d039726454ab02b21350000f582,

title = "Data Augmentation Technology for Dysarthria Assistive Systems",

abstract = "Voice-driven communication aids are one of the methods commonly used by patients with dysarthria. However, this type of assistive devices demands a large amount of voice data from patients to increase the effectiveness. In the meantime, this will sink patients into an overwhelming recording burden. Due to those difficulties, this research proposes a voice augmentation system to conquer the aforementioned concern. Furthermore, the system can improve the recognition efficiency. The results of this research reveal that the proposed speech generator system for dysarthria can launch corpus to be more similarities to the patient's speech. Moreover, the recognition rate, in duplicate sentences, has been improved and promoted to the higher level. The word error rate can be reduced from 64.42% to 4.39% in the case of patients with Free-talk. According to these results, our proposed system can provide more reliable and helpful technique for the development of communication aids.",

keywords = "Communication assistance system, Data augmentation, Deep learning, Dysarthria",

author = "Chu, {Wei Chung} and Hung, {Ying Hsiu} and Zheng, {Wei Zhong} and Lai, {Ying Hui}",

note = "Publisher Copyright: {\textcopyright} 2021 ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. All rights reserved.; 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 ; Conference date: 15-10-2021 Through 16-10-2021",

year = "2021",

language = "English",

series = "ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing",

publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",

pages = "144--150",

editor = "Lung-Hao Lee and Chia-Hui Chang and Kuan-Yu Chen",

booktitle = "ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing",

}

Chu, WC, Hung, YH, Zheng, WZ & Lai, YH 2021, Data Augmentation Technology for Dysarthria Assistive Systems. in L-H Lee, C-H Chang & K-Y Chen (eds), ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing, The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), pp. 144-150, 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021, Taoyuan, Taiwan, 15/10/21.

Data Augmentation Technology for Dysarthria Assistive Systems. / Chu, Wei Chung; Hung, Ying Hsiu; Zheng, Wei Zhong et al.
ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. ed. / Lung-Hao Lee; Chia-Hui Chang; Kuan-Yu Chen. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), 2021. p. 144-150 (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data Augmentation Technology for Dysarthria Assistive Systems

AU - Chu, Wei Chung

AU - Hung, Ying Hsiu

AU - Zheng, Wei Zhong

AU - Lai, Ying Hui

PY - 2021

Y1 - 2021

N2 - Voice-driven communication aids are one of the methods commonly used by patients with dysarthria. However, this type of assistive devices demands a large amount of voice data from patients to increase the effectiveness. In the meantime, this will sink patients into an overwhelming recording burden. Due to those difficulties, this research proposes a voice augmentation system to conquer the aforementioned concern. Furthermore, the system can improve the recognition efficiency. The results of this research reveal that the proposed speech generator system for dysarthria can launch corpus to be more similarities to the patient's speech. Moreover, the recognition rate, in duplicate sentences, has been improved and promoted to the higher level. The word error rate can be reduced from 64.42% to 4.39% in the case of patients with Free-talk. According to these results, our proposed system can provide more reliable and helpful technique for the development of communication aids.

AB - Voice-driven communication aids are one of the methods commonly used by patients with dysarthria. However, this type of assistive devices demands a large amount of voice data from patients to increase the effectiveness. In the meantime, this will sink patients into an overwhelming recording burden. Due to those difficulties, this research proposes a voice augmentation system to conquer the aforementioned concern. Furthermore, the system can improve the recognition efficiency. The results of this research reveal that the proposed speech generator system for dysarthria can launch corpus to be more similarities to the patient's speech. Moreover, the recognition rate, in duplicate sentences, has been improved and promoted to the higher level. The word error rate can be reduced from 64.42% to 4.39% in the case of patients with Free-talk. According to these results, our proposed system can provide more reliable and helpful technique for the development of communication aids.

KW - Communication assistance system

KW - Data augmentation

KW - Deep learning

KW - Dysarthria

UR - http://www.scopus.com/inward/record.url?scp=85127433502&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85127433502

T3 - ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing

SP - 144

EP - 150

BT - ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing

A2 - Lee, Lung-Hao

A2 - Chang, Chia-Hui

A2 - Chen, Kuan-Yu

PB - The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)

T2 - 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021

Y2 - 15 October 2021 through 16 October 2021

ER -

Chu WC, Hung YH, Zheng WZ, Lai YH. Data Augmentation Technology for Dysarthria Assistive Systems. In Lee LH, Chang CH, Chen KY, editors, ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP). 2021. p. 144-150. (ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing).

Data Augmentation Technology for Dysarthria Assistive Systems

Abstract

Publication series

Conference

Keywords

UN SDGs

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