CVC: Contrastive Learning for Non-Parallel Voice Conversion
Tingle Li, Yichen Liu, Chenxu Hu, Hang Zhao · 2021
Cycle consistent generative adversarial network (CycleGAN) and variational autoencoder (VAE) based models have gained popularity in non-parallel voice conversion recently.However, they often suffer from difficult training process and unsatisfactory results.In this paper, we propose CVC, a contrastive learning-based adversarial approach for voice conversion.Compared to previous CycleGAN-based methods, CVC only requires an efficient one-way GAN training by taking the advantage of contrastive learning.When it comes to nonparallel one-to-one voice conversion, CVC is on par or better than CycleGAN and VAE while effectively reducing training time.CVC further demonstrates superior performance in manyto-one voice conversion, enabling the conversion from unseen speakers.