Voice conversion refers to transferring speaker identity with well-preserved content. Better disentanglement of speech representations leads to better voice conversion. Recent studies have found that phonetic information from input audio has the potential ability to well represent content. Besides, the speaker-style modeling with pre-trained models making the process more complex. To tackle these issues, we introduce a new method named "CTVC" which utilizes disentangled speech representations with contrastive learning and time-invariant retrieval. Specifically, a similarity-based compression module is used to facilitate a more intimate connection between the frame-level hidden features and linguistic information at phoneme-level. Additionally, a time-invariant retrieval is proposed for timbre extraction based on multiple segmentations and mutual information. Experimental results demonstrate that "CTVC" outperforms previous studies and improves the sound quality and similarity of converted results.

翻译：语音转换是指在保留内容完整性的前提下进行说话人身份迁移。更好的语音表示解耦能带来更优的语音转换效果。近期研究发现，输入音频的语音信息具备良好的内容表征潜力，但基于预训练模型的说话人风格建模使得这一过程愈发复杂。针对上述问题，我们提出一种名为"CTVC"的新方法，该方法利用对比学习与时不变检索实现解耦语音表示。具体而言，采用基于相似度的压缩模块增强帧级隐层特征与音素级语言信息之间的紧密关联；同时，提出基于多重分割与互信息的时不变检索机制用于音色提取。实验结果表明，"CTVC"在转换结果的音质与相似度方面均优于以往研究。