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Voice conversion for highly expressive speech is challenging. Currentapproaches struggle with the balancing between speaker similarity,intelligibility and expressiveness. To address this problem, we proposeExpressive-VC, a novel end-to-end voice conversion framework that leveragesadvantages from both neural bottleneck feature (BNF) approach and informationperturbation approach. Specifically, we use a BNF encoder and a Perturbed-Wavencoder to form a content extractor to learn linguistic and para-linguisticfeatures respectively, where BNFs come from a robust pre-trained ASR model andthe perturbed wave becomes speaker-irrelevant after signal perturbation. Wefurther fuse the linguistic and para-linguistic features through an attentionmechanism, where speaker-dependent prosody features are adopted as theattention query, which result from a prosody encoder with target speakerembedding and normalized pitch and energy of source speech as input. Finallythe decoder consumes the integrated features and the speaker-dependent prosodyfeature to generate the converted speech. Experiments demonstrate thatExpressive-VC is superior to several state-of-the-art systems, achieving bothhigh expressiveness captured from the source speech and high speaker similaritywith the target speaker; meanwhile intelligibility is well maintained.

Expressive-VC: Highly Expressive Voice Conversion with Attention Fusion of Bottleneck and Perturbation Features