117 citations · 233 across the 41 of their papers we have counts for
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CasNet: Investigating Channel Robustness for Speech Separation
Fan-Lin Wang, Yao-Fei Cheng, Hung-Shin Lee +2
Recording channel mismatch between training and testing conditions has been shown to be a serious problem for speech separation. This situation greatly reduces the separation perfo…
Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition
Hung-Shin Lee, Yu Tsao, Shyh-Kang Jeng +1
Phonotactic constraints can be employed to distinguish languages by representing a speech utterance as a multinomial distribution or phone events. In the present study, we propose…
A Novel Speech Intelligibility Enhancement Model based on CanonicalCorrelation and Deep Learning
Tassadaq Hussain, Muhammad Diyan, Mandar Gogate +4
Current deep learning (DL) based approaches to speech intelligibility enhancement in noisy environments are often trained to minimise the feature distance between noise-free speech…
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…
Time Alignment using Lip Images for Frame-based Electrolaryngeal Voice Conversion
Yi-Syuan Liou, Wen-Chin Huang, Ming-Chi Yen +5
Voice conversion (VC) is an effective approach to electrolaryngeal (EL) speech enhancement, a task that aims to improve the quality of the artificial voice from an electrolarynx de…
A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion
Wen-Chin Huang, Kazuhiro Kobayashi, Yu-Huai Peng +4
We propose a new paradigm for maintaining speaker identity in dysarthric voice conversion (DVC). The poor quality of dysarthric speech can be greatly improved by statistical VC, bu…