# CVC: Contrastive Learning for Non-parallel Voice Conversion

arXiv: https://arxiv.org/abs/2011.00782 · HTML: https://mlsanity.com/paper/2011.00782/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Adversarially learning disentangled speech representations for robust multi-factor voice conversion](https://mlsanity.com/paper/2102.00184/) (2021)
- [Towards Improved Zero-shot Voice Conversion with Conditional DSVAE](https://mlsanity.com/paper/2205.05227/) (2022)
- [Self-Supervised Audio-Visual Soundscape Stylization](https://mlsanity.com/paper/2409.14340/) (2024)
- [An Adaptive Learning based Generative Adversarial Network for One-To-One Voice Conversion](https://mlsanity.com/paper/2104.12159/) (2021)

## Abstract (arXiv)

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 non-parallel 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 many-to-one voice conversion, enabling the conversion from unseen speakers.