Abstract
We introduce MaskVCT, a zero-shot voice conversion (VC) model that offers multi-factor controllability through multiple classifier-free guidances (CFGs). While previous VC models rely on a fixed conditioning scheme, MaskVCT integrates diverse conditions in a single model. To further enhance robustness and control, the model can leverage continuous or quantized linguistic features to enhance intellgibility and speaker similarity, and can use or omit pitch contour to control prosody. These choices allow users to seamlessly balance speaker identity, linguistic content, and prosodic factors in a zero-shot VC setting. Extensive experiments demonstrate that MaskVCT achieves the best target speaker and accent similarities while obtaining competitive word and character error rates compared to existing baselines. Audio samples are available at this https URL.
Abstract (translated)
我们介绍了MaskVCT，这是一种零样本语音转换（VC）模型，通过多种无分类器引导（CFGs）提供多因素可控性。与以往的VC模型依赖固定条件方案不同，MaskVCT在一个单一模型中整合了多样化的条件。为了进一步增强鲁棒性和控制能力，该模型可以利用连续或量化语言特征来提升可理解性和说话人相似度，并可以选择使用或忽略音高轮廓以控制语调。这些选择使用户能够在零样本VC环境中无缝平衡说话人身份、语言内容和语调因素。广泛的实验表明，MaskVCT在目标说话人的相似度和口音相似度方面表现最佳，在与现有基准相比时获得了具有竞争力的词错误率和字符错误率。音频样例可在[此链接](https://example.com)获取。（请注意，实际的URL应该替换为提供音频样本的具体链接）
URL
https://arxiv.org/abs/2509.17143
