| Autor: |
Chen KC, Yeh HW, Hang JY, Jhang SH, Zheng WZ, Lai YH |
| Jazyk: |
angličtina |
| Zdroj: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2019 Jul; Vol. 2019, pp. 1838-1841. |
| DOI: |
10.1109/EMBC.2019.8856560 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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