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Any-to-any voice conversion aims to convert the voice from and to anyspeakers even unseen during training, which is much more challenging comparedto one-to-one or many-to-many tasks, but much more attractive in real-worldscenarios. In this paper we proposed FragmentVC, in which the latent phoneticstructure of the utterance from the source speaker is obtained from Wav2Vec2.0, while the spectral features of the utterance(s) from the target speakerare obtained from log mel-spectrograms. By aligning the hidden structures ofthe two different feature spaces with a two-stage training process, FragmentVCis able to extract fine-grained voice fragments from the target speakerutterance(s) and fuse them into the desired utterance, all based on theattention mechanism of Transformer as verified with analysis on attention maps,and is accomplished end-to-end. This approach is trained with reconstructionloss only without any disentanglement considerations between content andspeaker information and doesn't require parallel data. Objective evaluationbased on speaker verification and subjective evaluation with MOS both showedthat this approach outperformed SOTA approaches, such as AdaIN-VC and AutoVC.

FragmentVC: Any-to-Any Voice Conversion by End-to-End Extracting and Fusing Fine-Grained Voice Fragments With Attention