262 citations · 589 across the 24 of their papers we have counts for
5 papers · 1 filter
Reinforcement Learning Based Speech Enhancement for Robust Speech Recognition
Yih-Liang Shen, Chao-Yuan Huang, Syu-Siang Wang +3
Conventional deep neural network (DNN)-based speech enhancement (SE) approaches aim to minimize the mean square error (MSE) between enhanced speech and clean reference. The MSE-opt…
Refined WaveNet Vocoder for Variational Autoencoder Based Voice Conversion
Wen-Chin Huang, Yi-Chiao Wu, Hsin-Te Hwang +6
This paper presents a refinement framework of WaveNet vocoders for variational autoencoder (VAE) based voice conversion (VC), which reduces the quality distortion caused by the mis…
Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders
Wen-Chin Huang, Hsin-Te Hwang, Yu-Huai Peng +2
An effective approach to non-parallel voice conversion (VC) is to utilize deep neural networks (DNNs), specifically variational auto encoders (VAEs), to model the latent structure…
Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM
Szu-Wei Fu, Yu Tsao, Hsin-Te Hwang +1
Nowadays, most of the objective speech quality assessment tools (e.g., perceptual evaluation of speech quality (PESQ)) are based on the comparison of the degraded/processed speech…
Noise Adaptive Speech Enhancement using Domain Adversarial Training
Chien-Feng Liao, Yu Tsao, Hung-Yi Lee +1
In this study, we propose a novel noise adaptive speech enhancement (SE) system, which employs a domain adversarial training (DAT) approach to tackle the issue of a noise type mism…