CVC: Contrastive Learning for Non-parallel Voice Conversion
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Cycle consistent generative adversarial network (CycleGAN) and variational autoencoder (VAE) based models have gained popularity in non-parallel voice conversion recently. However, they usually suffer from difficulty in model training and unsatisfactory results. In this paper, we propose CVC, a contrastive learning-based adversarial model for voice conversion. Compared to previous methods, CVC only requires one-way GAN training when it comes to non-parallel one-to-one voice conversion, while improving speech quality and reducing training time. CVC further demonstrates performance improvements in many-to-one voice conversion, enabling the conversion from unseen speakers.
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