1 citations · 1 across the 5 of their papers we have counts for
8 papers
SynthVC: Leveraging Synthetic Data for End-to-End Low Latency Streaming Voice Conversion
Zhao Guo, Ziqian Ning, Guobin Ma +1
Voice Conversion (VC) aims to modify a speaker's timbre while preserving linguistic content. While recent VC models achieve strong performance, most struggle in real-time streaming…
MeanVC: Lightweight and Streaming Zero-Shot Voice Conversion via Mean Flows
Guobin Ma, Jixun Yao, Ziqian Ning +4
Zero-shot voice conversion (VC) aims to transfer timbre from a source speaker to any unseen target speaker while preserving linguistic content. Growing application scenarios demand…
GenSE: Generative Speech Enhancement via Language Models using Hierarchical Modeling
Jixun Yao, Hexin Liu, Chen Chen +3
Semantic information refers to the meaning conveyed through words, phrases, and contextual relationships within a given linguistic structure. Humans can leverage semantic informati…
Fine-grained Preference Optimization Improves Zero-shot Text-to-Speech
Jixun Yao, Yuguang Yang, Yu Pan +5
Integrating human feedback to align text-to-speech (TTS) system outputs with human preferences has proven to be an effective approach for enhancing the robustness of language model…
ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training
Xinfa Zhu, Lei He, Yujia Xiao +4
Style voice conversion aims to transform the speaking style of source speech into a desired style while keeping the original speaker's identity. However, previous style voice conve…
StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching
Jixun Yao, Yuguang Yang, Yu Pan +4
Zero-shot voice conversion (VC) aims to transfer the timbre from the source speaker to an arbitrary unseen speaker while preserving the original linguistic content. Despite recent…