84 citations · 94 across the 10 of their papers we have counts for
10 papers
Exploiting Audio-Visual Features with Pretrained AV-HuBERT for Multi-Modal Dysarthric Speech Reconstruction
Xueyuan Chen, Yuejiao Wang, Xixin Wu +4
Dysarthric speech reconstruction (DSR) aims to transform dysarthric speech into normal speech by improving the intelligibility and naturalness. This is a challenging task especiall…
UNIT-DSR: Dysarthric Speech Reconstruction System Using Speech Unit Normalization
Yuejiao Wang, Xixin Wu, Disong Wang +2
Dysarthric speech reconstruction (DSR) systems aim to automatically convert dysarthric speech into normal-sounding speech. The technology eases communication with speakers affected…
Disentangled Speech Representation Learning for One-Shot Cross-lingual Voice Conversion Using -VAE
Hui Lu, Disong Wang, Xixin Wu +3
We propose an unsupervised learning method to disentangle speech into content representation and speaker identity representation. We apply this method to the challenging one-shot c…
Speaker Identity Preservation in Dysarthric Speech Reconstruction by Adversarial Speaker Adaptation
Disong Wang, Songxiang Liu, Xixin Wu +4
Dysarthric speech reconstruction (DSR), which aims to improve the quality of dysarthric speech, remains a challenge, not only because we need to restore the speech to be normal, bu…
VCVTS: Multi-speaker Video-to-Speech synthesis via cross-modal knowledge transfer from voice conversion
Disong Wang, Shan Yang, Dan Su +3
Though significant progress has been made for speaker-dependent Video-to-Speech (VTS) synthesis, little attention is devoted to multi-speaker VTS that can map silent video to speec…
VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion
Disong Wang, Liqun Deng, Yu Ting Yeung +3
One-shot voice conversion (VC), which performs conversion across arbitrary speakers with only a single target-speaker utterance for reference, can be effectively achieved by speech…
Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization
Disong Wang, Liqun Deng, Yu Ting Yeung +3
Dysarthric speech detection (DSD) systems aim to detect characteristics of the neuromotor disorder from speech. Such systems are particularly susceptible to domain mismatch where t…
Improved End-to-End Dysarthric Speech Recognition via Meta-learning Based Model Re-initialization
Disong Wang, Jianwei Yu, Xixin Wu +3
Dysarthric speech recognition is a challenging task as dysarthric data is limited and its acoustics deviate significantly from normal speech. Model-based speaker adaptation is a pr…
Learning Explicit Prosody Models and Deep Speaker Embeddings for Atypical Voice Conversion
Disong Wang, Songxiang Liu, Lifa Sun +3
Though significant progress has been made for the voice conversion (VC) of typical speech, VC for atypical speech, e.g., dysarthric and second-language (L2) speech, remains a chall…
Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence Modeling
Songxiang Liu, Yuewen Cao, Disong Wang +3
This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq), non-parallel voice conversion approach, which utilizes text supervision during training. In th…