MulliVC: Multi-lingual Voice Conversion With Cycle Consistency
<|reference_start|>MulliVC: Multi-lingual Voice Conversion With Cycle Consistency: Voice conversion aims to modify the source speaker's voice to resemble the target speaker while preserving the original speech content. Despite notable advancements in voice conversion these days, multi-lingual voice conversion (including both monolingual and cross-lingual scenarios) has yet to be extensively studied. It faces two main challenges: 1) the considerable variability in prosody and articulation habits across languages; and 2) the rarity of paired multi-lingual datasets from the same speaker. In this paper, we propose MulliVC, a novel voice conversion system that only converts timbre and keeps original content and source language prosody without multi-lingual paired data. Specifically, each training step of MulliVC contains three substeps: In step one the model is trained with monolingual speech data; then, steps two and three take inspiration from back translation, construct a cyclical process to disentangle the timbre and other information (content, prosody, and other language-related information) in the absence of multi-lingual data from the same speaker. Both objective and subjective results indicate that MulliVC significantly surpasses other methods in both monolingual and cross-lingual contexts, demonstrating the system's efficacy and the viability of the three-step approach with cycle consistency. Audio samples can be found on our demo page (mullivc.github.io).<|reference_end|>
@article{huang2024mullivc:,
    title={MulliVC: Multi-lingual Voice Conversion With Cycle Consistency},
    author={Jiawei Huang, Chen Zhang, Yi Ren, Ziyue Jiang, Zhenhui Ye, Jinglin
  Liu, Jinzheng He, Xiang Yin, Zhou Zhao},
    journal={arXiv preprint arXiv:2408.04708},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04708},
    primaryClass={cs.SD cs.AI eess.AS}
}