@inproceedings{1856a878bffd49c4bce325a084911ce1,
title = "A joint-feature learning-based voice conversion system for dysarthric user based on deep learning technology",
abstract = "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.",
author = "Chen, {Ko Chiang} and Yeh, {Hsiu Wei} and Hang, {Ji Yan} and Jhang, {Sin Hua} and Zheng, {Wei Zhong} and Lai, {Ying Hui}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 ; Conference date: 23-07-2019 Through 27-07-2019",
year = "2019",
month = jul,
doi = "10.1109/EMBC.2019.8856560",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1838--1841",
booktitle = "2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019",
address = "美國",
}