9 citations · 24 across the 23 of their papers we have counts for
23 papers
SemBridge: Semantic Token Anchoring for Continuous-Latent Autoregressive Speech Generation
Hanke Xie, Haopeng Lin, Jiale Qian +13
Continuous-latent autoregressive speech generation has emerged as a promising alternative to discrete-token modeling by avoiding quantization loss and preserving richer acoustic in…
StreamVoice+: Evolving into End-to-end Streaming Zero-shot Voice Conversion
Zhichao Wang, Yuanzhe Chen, Xinsheng Wang +2
StreamVoice has recently pushed the boundaries of zero-shot voice conversion (VC) in the streaming domain. It uses a streamable language model (LM) with a context-aware approach to…
Vec-Tok-VC+: Residual-enhanced Robust Zero-shot Voice Conversion with Progressive Constraints in a Dual-mode Training Strategy
Linhan Ma, Xinfa Zhu, Yuanjun Lv +5
Zero-shot voice conversion (VC) aims to transform source speech into arbitrary unseen target voice while keeping the linguistic content unchanged. Recent VC methods have made signi…
DualVC 3: Leveraging Language Model Generated Pseudo Context for End-to-end Low Latency Streaming Voice Conversion
Ziqian Ning, Shuai Wang, Pengcheng Zhu +4
Streaming voice conversion has become increasingly popular for its potential in real-time applications. The recently proposed DualVC 2 has achieved robust and high-quality streamin…
StreamVoice: Streamable Context-Aware Language Modeling for Real-time Zero-Shot Voice Conversion
Zhichao Wang, Yuanzhe Chen, Xinsheng Wang +2
Recent language model (LM) advancements have showcased impressive zero-shot voice conversion (VC) performance. However, existing LM-based VC models usually apply offline conversion…
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…
VITS-Based Singing Voice Conversion Leveraging Whisper and multi-scale F0 Modeling
Ziqian Ning, Yuepeng Jiang, Zhichao Wang +2
This paper introduces the T23 team's system submitted to the Singing Voice Conversion Challenge 2023. Following the recognition-synthesis framework, our singing conversion model is…
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…
Text-to-Video: a Two-stage Framework for Zero-shot Identity-agnostic Talking-head Generation
Zhichao Wang, Mengyu Dai, Keld Lundgaard
The advent of ChatGPT has introduced innovative methods for information gathering and analysis. However, the information provided by ChatGPT is limited to text, and the visualizati…
LM-VC: Zero-shot Voice Conversion via Speech Generation based on Language Models
Zhichao Wang, Yuanzhe Chen, Lei Xie +2
Language model (LM) based audio generation frameworks, e.g., AudioLM, have recently achieved new state-of-the-art performance in zero-shot audio generation. In this paper, we explo…
Multi-level Temporal-channel Speaker Retrieval for Zero-shot Voice Conversion
Zhichao Wang, Liumeng Xue, Qiuqiang Kong +4
Zero-shot voice conversion (VC) converts source speech into the voice of any desired speaker using only one utterance of the speaker without requiring additional model updates. Typ…
Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features
Ziqian Ning, Qicong Xie, Pengcheng Zhu +5
Voice conversion for highly expressive speech is challenging. Current approaches struggle with the balancing between speaker similarity, intelligibility and expressiveness. To addr…
Delivering Speaking Style in Low-resource Voice Conversion with Multi-factor Constraints
Zhichao Wang, Xinsheng Wang, Lei Xie +3
Conveying the linguistic content and maintaining the source speech's speaking style, such as intonation and emotion, is essential in voice conversion (VC). However, in a low-resour…
AccentSpeech: Learning Accent from Crowd-sourced Data for Target Speaker TTS with Accents
Yongmao Zhang, Zhichao Wang, Peiji Yang +3
Learning accent from crowd-sourced data is a feasible way to achieve a target speaker TTS system that can synthesize accent speech. To this end, there are two challenging problems…
Streaming Voice Conversion Via Intermediate Bottleneck Features And Non-streaming Teacher Guidance
Yuanzhe Chen, Ming Tu, Tang Li +7
Streaming voice conversion (VC) is the task of converting the voice of one person to another in real-time. Previous streaming VC methods use phonetic posteriorgrams (PPGs) extracte…
Cross-speaker Emotion Transfer Based On Prosody Compensation for End-to-End Speech Synthesis
Tao Li, Xinsheng Wang, Qicong Xie +3
Cross-speaker emotion transfer speech synthesis aims to synthesize emotional speech for a target speaker by transferring the emotion from reference speech recorded by another (sour…
IQDUBBING: Prosody modeling based on discrete self-supervised speech representation for expressive voice conversion
Wendong Gan, Bolong Wen, Ying Yan +6
Prosody modeling is important, but still challenging in expressive voice conversion. As prosody is difficult to model, and other factors, e.g., speaker, environment and content, wh…
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…
Cross-speaker emotion disentangling and transfer for end-to-end speech synthesis
Tao Li, Xinsheng Wang, Qicong Xie +2
The cross-speaker emotion transfer task in text-to-speech (TTS) synthesis particularly aims to synthesize speech for a target speaker with the emotion transferred from reference sp…
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…
WNARS: WFST based Non-autoregressive Streaming End-to-End Speech Recognition
Zhichao Wang, Wenwen Yang, Pan Zhou +1
Recently, attention-based encoder-decoder (AED) end-to-end (E2E) models have drawn more and more attention in the field of automatic speech recognition (ASR). AED models, however,…
Accent and Speaker Disentanglement in Many-to-many Voice Conversion
Zhichao Wang, Wenshuo Ge, Xiong Wang +6
This paper proposes an interesting voice and accent joint conversion approach, which can convert an arbitrary source speaker's voice to a target speaker with non-native accent. Thi…