1 citations · 1 across the 6 of their papers we have counts for
6 papers
Vec-Tok Speech: speech vectorization and tokenization for neural speech generation
Xinfa Zhu, Yuanjun Lv, Yi Lei +5
Language models (LMs) have recently flourished in natural language processing and computer vision, generating high-fidelity texts or images in various tasks. In contrast, the curre…
U-Style: Cascading U-nets with Multi-level Speaker and Style Modeling for Zero-Shot Voice Cloning
Tao Li, Zhichao Wang, Xinfa Zhu +4
Zero-shot speaker cloning aims to synthesize speech for any target speaker unseen during TTS system building, given only a single speech reference of the speaker at hand. Although…
MSM-VC: High-fidelity Source Style Transfer for Non-Parallel Voice Conversion by Multi-scale Style Modeling
Zhichao Wang, Xinsheng Wang, Qicong Xie +4
In addition to conveying the linguistic content from source speech to converted speech, maintaining the speaking style of source speech also plays an important role in the voice co…
Multi-speaker Multi-style Text-to-speech Synthesis With Single-speaker Single-style Training Data Scenarios
Qicong Xie, Tao Li, Xinsheng Wang +4
In the existing cross-speaker style transfer task, a source speaker with multi-style recordings is necessary to provide the style for a target speaker. However, it is hard for one…
One-shot Voice Conversion For Style Transfer Based On Speaker Adaptation
Zhichao Wang, Qicong Xie, Tao Li +4
One-shot style transfer is a challenging task, since training on one utterance makes model extremely easy to over-fit to training data and causes low speaker similarity and lack of…
Enriching Source Style Transfer in Recognition-Synthesis based Non-Parallel Voice Conversion
Zhichao Wang, Xinyong Zhou, Fengyu Yang +6
Current voice conversion (VC) methods can successfully convert timbre of the audio. As modeling source audio's prosody effectively is a challenging task, there are still limitation…