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Conveying the linguistic content and maintaining the source speech's speakingstyle, such as intonation and emotion, is essential in voice conversion (VC).However, in a low-resource situation, where only limited utterances from thetarget speaker are accessible, existing VC methods are hard to meet thisrequirement and capture the target speaker's timber. In this work, a novel VCmodel, referred to as MFC-StyleVC, is proposed for the low-resource VC task.Specifically, speaker timbre constraint generated by clustering method is newlyproposed to guide target speaker timbre learning in different stages.Meanwhile, to prevent over-fitting to the target speaker's limited data,perceptual regularization constraints explicitly maintain model performance onspecific aspects, including speaking style, linguistic content, and speechquality. Besides, a simulation mode is introduced to simulate the inferenceprocess to alleviate the mismatch between training and inference. Extensiveexperiments performed on highly expressive speech demonstrate the superiorityof the proposed method in low-resource VC.

Delivering Speaking Style in Low-resource Voice Conversion with Multi-factor Constraints