Discl-VC：解耦离散标记与上下文学习用于可控零样本语音转换
Discl-VC: Disentangled Discrete Tokens and In-Context Learning for Controllable Zero-Shot Voice Conversion
AI总结：
提出Discl-VC框架，通过解耦自监督语音表示中的内容与韵律信息，结合流匹配变换器上下文学习和掩码生成变换器预测离散韵律标记，实现了高精度的零样本语音转换与韵律控制。
摘要：
目前，零样本语音转换系统能够合成未见说话人的声音。然而，大多数现有方法难以准确复制源说话人的说话风格或模仿目标说话人的独特说话风格，从而限制了语音转换的可控性。在本研究中，我们提出了Discl-VC，这是一种新颖的语音转换框架，它从自监督语音表示中解耦内容和韵律信息，并通过基于流匹配变换器的上下文学习来合成目标说话人的声音。为了实现对生成语音韵律的精确控制，我们引入了一种掩码生成变换器，它基于提示词以非自回归方式预测离散韵律标记。实验结果表明，Discl-VC在零样本语音转换中表现优异，并且在合成语音的韵律控制方面具有显著的准确性。
英文摘要：
Currently, zero-shot voice conversion systems are capable of synthesizing the voice of unseen speakers. However, most existing approaches struggle to accurately replicate the speaking style of the source speaker or mimic the distinctive speaking style of the target speaker, thereby limiting the controllability of voice conversion. In this work, we propose Discl-VC, a novel voice conversion framework that disentangles content and prosody information from self-supervised speech representations and synthesizes the target speaker's voice through in-context learning with a flow matching transformer. To enable precise control over the prosody of generated speech, we introduce a mask generative transformer that predicts discrete prosody tokens in a non-autoregressive manner based on prompts. Experimental results demonstrate the superior performance of Discl-VC in zero-shot voice conversion and its remarkable accuracy in prosody control for synthesized speech.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
