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Voice conversion as the style transfer task applied to speech, refers toconverting one person's speech into a new speech that sounds like anotherperson's. Up to now, there has been a lot of research devoted to betterimplementation of VC tasks. However, a good voice conversion model should notonly match the timbre information of the target speaker, but also expressiveinformation such as prosody, pace, pause, etc. In this context, prosodymodeling is crucial for achieving expressive voice conversion that soundsnatural and convincing. Unfortunately, prosody modeling is important butchallenging, especially without text transcriptions. In this paper, we firstlypropose a novel voice conversion framework named 'PMVC', which effectivelyseparates and models the content, timbre, and prosodic information from thespeech without text transcriptions. Specially, we introduce a new speechaugmentation algorithm for robust prosody extraction. And building upon this,mask and predict mechanism is applied in the disentanglement of prosody andcontent information. The experimental results on the AIShell-3 corpus supportsour improvement of naturalness and similarity of converted speech.

PMVC: Data Augmentation-Based Prosody Modeling for Expressive Voice Conversion