EAD-VC：利用IFUB估计器与联合文本引导一致性学习增强语音自动解耦以实现语音转换
EAD-VC: Enhancing Speech Auto-Disentanglement for Voice Conversion with IFUB Estimator and Joint Text-Guided Consistent Learning
AI总结：
本文提出EAD-VC两阶段自监督语音解耦模型，通过IFUB互信息上界估计器分离语音成分重叠信息，并引入联合文本引导一致性模块消除音色泄漏，在语音转换中提升解耦有效性、自然度和相似度。
摘要：
利用无监督学习将语音解耦为内容、节奏、音高和音色以进行语音转换已成为一个热门研究课题。现有工作通常通过人工设计的瓶颈特征来考虑解耦语音成分，这无法实现充分的信息解耦，而音高和节奏仍可能混合在一起。在解耦过程中存在信息重叠的风险，从而导致语音自然度降低。为克服这些限制，我们提出了一种两阶段模型，以自监督方式解耦语音表示，无需人工设计的瓶颈结构，该模型利用互信息（MI）与所设计的上界估计器（IFUB）来分离语音成分之间的重叠信息。此外，我们设计了一个联合文本引导一致性（TGC）模块，以引导语音内容的提取并消除音色泄漏问题。实验表明，在解耦有效性、语音自然度和相似度方面，我们的模型能够取得优于基线的性能。音频样本可在 https://largeaudiomodel.com/eadvc 找到。
英文摘要：
Using unsupervised learning to disentangle speech into content, rhythm, pitch, and timbre for voice conversion has become a hot research topic. Existing works generally take into account disentangling speech components through human-crafted bottleneck features which can not achieve sufficient information disentangling, while pitch and rhythm may still be mixed together. There is a risk of information overlap in the disentangling process which results in less speech naturalness. To overcome such limits, we propose a two-stage model to disentangle speech representations in a self-supervised manner without a human-crafted bottleneck design, which uses the Mutual Information (MI) with the designed upper bound estimator (IFUB) to separate overlapping information between speech components. Moreover, we design a Joint Text-Guided Consistent (TGC) module to guide the extraction of speech content and eliminate timbre leakage issues. Experiments show that our model can achieve a better performance than the baseline, regarding disentanglement effectiveness, speech naturalness, and similarity. Audio samples can be found at https://largeaudiomodel.com/eadvc.
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。
