56 citations · 166 across the 19 of their papers we have counts for
7 papers · 1 filter
Improving Speech Enhancement Performance by Leveraging Contextual Broad Phonetic Class Information
Yen-Ju Lu, Chia-Yu Chang, Cheng Yu +4
Previous studies have confirmed that by augmenting acoustic features with the place/manner of articulatory features, the speech enhancement (SE) process can be guided to consider t…
Multi-Scale Networks for 3D Human Pose Estimation with Inference Stage Optimization
Cheng Yu, Bo Wang, Bo Yang +1
Estimating 3D human poses from a monocular video is still a challenging task. Many existing methods' performance drops when the target person is occluded by other objects, or the m…
HLT-NUS Submission for NIST 2019 Multimedia Speaker Recognition Evaluation
Rohan Kumar Das, Ruijie Tao, Jichen Yang +3
This work describes the speaker verification system developed by Human Language Technology Laboratory, National University of Singapore (HLT-NUS) for 2019 NIST Multimedia Speaker R…
Improving Perceptual Quality by Phone-Fortified Perceptual Loss using Wasserstein Distance for Speech Enhancement
Tsun-An Hsieh, Cheng Yu, Szu-Wei Fu +2
Speech enhancement (SE) aims to improve speech quality and intelligibility, which are both related to a smooth transition in speech segments that may carry linguistic information,…
Waveform-based Voice Activity Detection Exploiting Fully Convolutional networks with Multi-Branched Encoders
Cheng Yu, Kuo-Hsuan Hung, I-Fan Lin +3
In this study, we propose an encoder-decoder structured system with fully convolutional networks to implement voice activity detection (VAD) directly on the time-domain waveform. T…
Boosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing
Szu-Wei Fu, Chien-Feng Liao, Tsun-An Hsieh +9
The Transformer architecture has demonstrated a superior ability compared to recurrent neural networks in many different natural language processing applications. Therefore, our st…
Speech Enhancement based on Denoising Autoencoder with Multi-branched Encoders
Cheng Yu, Ryandhimas E. Zezario, Syu-Siang Wang +5
Deep learning-based models have greatly advanced the performance of speech enhancement (SE) systems. However, two problems remain unsolved, which are closely related to model gener…