MulliVC: Multi-lingual Voice Conversion With Cycle Consistency

• URL: http://arxiv.org/abs/2408.04708v1
• Date: Thu, 8 Aug 2024 18:12:51 GMT
• Title: MulliVC: Multi-lingual Voice Conversion With Cycle Consistency
• Authors: Jiawei Huang, Chen Zhang, Yi Ren, Ziyue Jiang, Zhenhui Ye, Jinglin Liu, Jinzheng He, Xiang Yin, Zhou Zhao,
• Abstract summary: MulliVC is a novel voice conversion system that only converts timbre and keeps original content and source language prosody without multi-lingual paired data. Both objective and subjective results indicate that MulliVC significantly surpasses other methods in both monolingual and cross-lingual contexts.
• Score: 75.59590240034261
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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).

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