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

• URL: http://arxiv.org/abs/2302.08137v1
• Date: Thu, 16 Feb 2023 08:10:41 GMT
• Title: ACE-VC: Adaptive and Controllable Voice Conversion using Explicitly Disentangled Self-supervised Speech Representations
• Authors: Shehzeen Hussain, Paarth Neekhara, Jocelyn Huang, Jason Li, Boris Ginsburg
• Abstract summary: We propose a zero-shot voice conversion method using speech representations trained with self-supervised learning. We develop a multi-task model to decompose a speech utterance into features such as linguistic content, speaker characteristics, and speaking style. Next, we develop a synthesis model with pitch and duration predictors that can effectively reconstruct the speech signal from its representation.
• Score: 12.20522794248598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose a zero-shot voice conversion method using speech representations trained with self-supervised learning. First, we develop a multi-task model to decompose a speech utterance into features such as linguistic content, speaker characteristics, and speaking style. To disentangle content and speaker representations, we propose a training strategy based on Siamese networks that encourages similarity between the content representations of the original and pitch-shifted audio. Next, we develop a synthesis model with pitch and duration predictors that can effectively reconstruct the speech signal from its decomposed representation. Our framework allows controllable and speaker-adaptive synthesis to perform zero-shot any-to-any voice conversion achieving state-of-the-art results on metrics evaluating speaker similarity, intelligibility, and naturalness. Using just 10 seconds of data for a target speaker, our framework can perform voice swapping and achieves a speaker verification EER of 5.5% for seen speakers and 8.4% for unseen speakers.

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