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In this work, we propose a zero-shot voice conversion method using speechrepresentations trained with self-supervised learning. First, we develop amulti-task model to decompose a speech utterance into features such aslinguistic content, speaker characteristics, and speaking style. To disentanglecontent and speaker representations, we propose a training strategy based onSiamese networks that encourages similarity between the content representationsof the original and pitch-shifted audio. Next, we develop a synthesis modelwith pitch and duration predictors that can effectively reconstruct the speechsignal from its decomposed representation. Our framework allows controllableand speaker-adaptive synthesis to perform zero-shot any-to-any voice conversionachieving state-of-the-art results on metrics evaluating speaker similarity,intelligibility, and naturalness. Using just 10 seconds of data for a targetspeaker, our framework can perform voice swapping and achieves a speakerverification EER of 5.5% for seen speakers and 8.4% for unseen speakers.

ACE-VC: Adaptive and Controllable Voice Conversion using Explicitly Disentangled Self-supervised Speech Representations