9 citations · 14 across the 6 of their papers we have counts for
6 papers
Preference-based training framework for automatic speech quality assessment using deep neural network
Cheng-Hung Hu, Yusuke Yasuda, Tomoki Toda
One objective of Speech Quality Assessment (SQA) is to estimate the ranks of synthetic speech systems. However, recent SQA models are typically trained using low-precision direct s…
NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling
Chi-Chang Lee, Cheng-Hung Hu, Yu-Chen Lin +3
For deep learning-based speech enhancement (SE) systems, the training-test acoustic mismatch can cause notable performance degradation. To address the mismatch issue, numerous nois…
SVSNet: An End-to-end Speaker Voice Similarity Assessment Model
Cheng-Hung Hu, Yu-Huai Peng, Junichi Yamagishi +2
Neural evaluation metrics derived for numerous speech generation tasks have recently attracted great attention. In this paper, we propose SVSNet, the first end-to-end neural networ…
Relational Data Selection for Data Augmentation of Speaker-dependent Multi-band MelGAN Vocoder
Yi-Chiao Wu, Cheng-Hung Hu, Hung-Shin Lee +5
Nowadays, neural vocoders can generate very high-fidelity speech when a bunch of training data is available. Although a speaker-dependent (SD) vocoder usually outperforms a speaker…
The AS-NU System for the M2VoC Challenge
Cheng-Hung Hu, Yi-Chiao Wu, Wen-Chin Huang +6
This paper describes the AS-NU systems for two tracks in MultiSpeaker Multi-Style Voice Cloning Challenge (M2VoC). The first track focuses on using a small number of 100 target utt…
The Academia Sinica Systems of Voice Conversion for VCC2020
Yu-Huai Peng, Cheng-Hung Hu, Alexander Kang +4
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 v…