8 citations · 14 across the 3 of their papers we have counts for
8 papers
Partially Fake Audio Detection by Self-attention-based Fake Span Discovery
Haibin Wu, Heng-Cheng Kuo, Naijun Zheng +5
The past few years have witnessed the significant advances of speech synthesis and voice conversion technologies. However, such technologies can undermine the robustness of broadly…
MetricGAN-U: Unsupervised speech enhancement/ dereverberation based only on noisy/ reverberated speech
Szu-Wei Fu, Cheng Yu, Kuo-Hsuan Hung +2
Most of the deep learning-based speech enhancement models are learned in a supervised manner, which implies that pairs of noisy and clean speech are required during training. Conse…
EMA2S: An End-to-End Multimodal Articulatory-to-Speech System
Yu-Wen Chen, Kuo-Hsuan Hung, Shang-Yi Chuang +4
Synthesized speech from articulatory movements can have real-world use for patients with vocal cord disorders, situations requiring silent speech, or in high-noise environments. In…
Deep Learning Based Signal Enhancement of Low-Resolution Accelerometer for Fall Detection Systems
Kai-Chun Liu, Kuo-Hsuan Hung, Chia-Yeh Hsieh +3
In the last two decades, fall detection (FD) systems have been developed as a popular assistive technology. Such systems automatically detect critical fall events and immediately a…
A Study of Incorporating Articulatory Movement Information in Speech Enhancement
Yu-Wen Chen, Kuo-Hsuan Hung, Shang-Yi Chuang +3
Although deep learning algorithms are widely used for improving speech enhancement (SE) performance, the performance remains limited under highly challenging conditions, such as un…
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…