no code implementations • 6 Oct 2020 • Yu-Huai Peng, Cheng-Hung Hu, Alexander Kang, Hung-Shin Lee, Pin-Yuan Chen, Yu Tsao, Hsin-Min Wang
This paper describes the Academia Sinica systems for the two tasks of Voice Conversion Challenge 2020, namely voice conversion within the same language (Task 1) and cross-lingual voice conversion (Task 2).
no code implementations • 7 Apr 2021 • Cheng-Hung Hu, Yi-Chiao Wu, Wen-Chin Huang, Yu-Huai Peng, Yu-Wen Chen, Pin-Jui Ku, Tomoki Toda, Yu Tsao, Hsin-Min Wang
The first track focuses on using a small number of 100 target utterances for voice cloning, while the second track focuses on using only 5 target utterances for voice cloning.
no code implementations • 10 Jun 2021 • Yi-Chiao Wu, Cheng-Hung Hu, Hung-Shin Lee, Yu-Huai Peng, Wen-Chin Huang, Yu Tsao, Hsin-Min Wang, Tomoki Toda
Nowadays, neural vocoders can generate very high-fidelity speech when a bunch of training data is available.
no code implementations • 20 Jul 2021 • Cheng-Hung Hu, Yu-Huai Peng, Junichi Yamagishi, Yu Tsao, Hsin-Min Wang
Neural evaluation metrics derived for numerous speech generation tasks have recently attracted great attention.
no code implementations • 18 Jun 2022 • Chi-Chang Lee, Cheng-Hung Hu, Yu-Chen Lin, Chu-Song Chen, Hsin-Min Wang, Yu Tsao
NASTAR uses a feedback mechanism to simulate adaptive training data via a noise extractor and a retrieval model.
no code implementations • 29 Aug 2023 • Cheng-Hung Hu, Yusuke Yasuda, Tomoki Toda
We propose a training framework of SQA models that can be trained with only preference scores derived from pairs of MOS to improve ranking prediction.