262 citations · 1.4k across the 109 of their papers we have counts for
10 papers · 1 filter
Robustness against the channel effect in pathological voice detection
Yi-Te Hsu, Zining Zhu, Chi-Te Wang +3
Many people are suffering from voice disorders, which can adversely affect the quality of their lives. In response, some researchers have proposed algorithms for automatic assessme…
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…
Speech Enhancement Based on Reducing the Detail Portion of Speech Spectrograms in Modulation Domain via Discrete Wavelet Transform
Shih-kuang Lee, Syu-Siang Wang, Yu Tsao +1
In this paper, we propose a novel speech enhancement (SE) method by exploiting the discrete wavelet transform (DWT). This new method reduces the amount of fast time-varying portion…
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…
Generative Adversarial Networks for Unpaired Voice Transformation on Impaired Speech
Li-Wei Chen, Hung-Yi Lee, Yu Tsao
This paper focuses on using voice conversion (VC) to improve the speech intelligibility of surgical patients who have had parts of their articulators removed. Due to the difficulty…
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…
A study on speech enhancement using exponent-only floating point quantized neural network (EOFP-QNN)
Yi-Te Hsu, Yu-Chen Lin, Szu-Wei Fu +2
Numerous studies have investigated the effectiveness of neural network quantization on pattern classification tasks. The present study, for the first time, investigated the perform…
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…
Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm
Wei-Jen Lee, Syu-Siang Wang, Fei Chen +3
Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a comp…