no code implementations • 3 Feb 2023 • Huan-Hsin Tseng, Hsin-Yi Lin, Kuo-Hsuan Hung, Yu Tsao
The method shows an increase in efficiency and accuracy for domain adaptation.
no code implementations • 11 Nov 2022 • Hsin-Yi Lin, Huan-Hsin Tseng, Yu Tsao
It has been shown recently that deep learning based models are effective on speech quality prediction and could outperform traditional metrics in various perspectives.
1 code implementation • 7 Apr 2022 • Kuo-Hsuan Hung, Szu-Wei Fu, Huan-Hsin Tseng, Hsin-Tien Chiang, Yu Tsao, Chii-Wann Lin
We further study the relationship between the noise robustness of SSL representation via clean-noisy distance (CN distance) and the layer importance for SE.
Ranked #7 on Speech Enhancement on VoiceBank + DEMAND
no code implementations • 5 Dec 2021 • Heng-Cheng Kuo, Yu-Peng Hsieh, Huan-Hsin Tseng, Chi-Tei Wang, Shih-Hau Fang, Yu Tsao
Conclusion: By deploying factorized convolutional neural networks and domain adversarial training, domain-invariant features can be derived for voice disorder classification with limited resources.
1 code implementation • NeurIPS 2021 • Hsin-Yi Lin, Huan-Hsin Tseng, Xugang Lu, Yu Tsao
This paper presents a novel discriminator-constrained optimal transport network (DOTN) that performs unsupervised domain adaptation for speech enhancement (SE), which is an essential regression task in speech processing.
no code implementations • 7 Dec 2020 • Tsai-Min Chen, Yuan-Hong Tsai, Huan-Hsin Tseng, Kai-Chun Liu, Jhih-Yu Chen, Chih-Han Huang, Guo-Yuan Li, Chun-Yen Shen, Yu Tsao
In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG.