One-shot voice conversion (VC), which performs conversion across arbitrary
speakers with only a single target-speaker utterance for reference, can be
effectively achieved by speech representation disentanglement. Existing work
generally ignores the correlation between different speech representations
during training, which causes leakage of content information into the speaker
representation and thus degrades VC performance. To alleviate this issue, we
employ vector quantization (VQ) for content encoding and introduce mutual
information (MI) as the correlation metric during training, to achieve proper
disentanglement of content, speaker and pitch representations, by reducing
their inter-dependencies in an unsupervised manner. Experimental results
reflect the superiority of the proposed method in learning effective
disentangled speech representations for retaining source linguistic content and
intonation variations, while capturing target speaker characteristics. In doing
so, the proposed approach achieves higher speech naturalness and speaker
similarity than current state-of-the-art one-shot VC systems. Our code,
pre-trained models and demo are available at
this https URL

本文提出通过使用向量量化 (VQ) 进行内容编码，并在训练期间引入互信息 (MI) 作为相关度度量，从而在无监督的情况下实现内容、说话人和音高表示的适当分离，以提高单次语音转换系统的性能。该方法在保留源语言内容和语调变化的同时，捕捉目标说话人的特征，实现了比现有单次语音转换系统更高的语音自然度和说话人相似度。