A joint-feature learning-based voice conversion system for dysarthric user based on deep learning technology

Ko Chiang Chen, Hsiu Wei Yeh, Ji Yan Hang, Sin Hua Jhang, Wei Zhong Zheng, Ying Hui Lai*

*此作品的通信作者

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

Dysarthria speakers suffer from poor communication, and voice conversion (VC) technology is a potential approach for improving their speech quality. This study presents a joint feature learning approach to improve a sub-band deep neural network-based VC system, termed J-SBDNN. In this study, a listening test of speech intelligibility is used to confirm the benefits of the proposed J-SBDNN VC system, with several well-known VC approaches being used for comparison. The results showed that the J-SBDNN VC system provided a higher speech intelligibility performance than other VC approaches in most test conditions. It implies that the J-SBDNN VC system could potentially be used as one of the electronic assistive technologies to improve the speech quality for a dysarthric speaker.

原文English
主出版物標題2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1838-1841
頁數4
ISBN(電子)9781538613115
DOIs
出版狀態Published - 7月 2019
事件41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
持續時間: 23 7月 201927 7月 2019

出版系列

名字Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN(列印)1557-170X

Conference

Conference41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
國家/地區Germany
城市Berlin
期間23/07/1927/07/19

指紋

深入研究「A joint-feature learning-based voice conversion system for dysarthric user based on deep learning technology」主題。共同形成了獨特的指紋。

引用此