# High-quality nonparallel voice conversion based on cycle-consistent adversarial network

arXiv: https://arxiv.org/abs/1804.00425 · HTML: https://mlsanity.com/paper/1804.00425/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2018.

## Built on by (most cited first, 13 of 13)

- [Emotional Voice Conversion: Theory, Databases and ESD](https://mlsanity.com/paper/2105.14762/) (2021)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion](https://mlsanity.com/paper/2208.08757/) (2022)
- [NVC-Net: End-to-End Adversarial Voice Conversion](https://mlsanity.com/paper/2106.00992/) (2021)
- [How Deep Are the Fakes? Focusing on Audio Deepfake: A Survey](https://mlsanity.com/paper/2111.14203/) (2021)
- [Towards General-Purpose Text-Instruction-Guided Voice Conversion](https://mlsanity.com/paper/2309.14324/) (2023)
- [Voicy: Zero-Shot Non-Parallel Voice Conversion in Noisy Reverberant Environments](https://mlsanity.com/paper/2106.08873/) (2021)
- [Catch You and I Can: Revealing Source Voiceprint Against Voice Conversion](https://mlsanity.com/paper/2302.12434/) (2023)
- [A Comparative Analysis Of Latent Regressor Losses For Singing Voice Conversion](https://mlsanity.com/paper/2302.13678/) (2023)
- [Beyond Voice Identity Conversion: Manipulating Voice Attributes by Adversarial Learning of Structured Disentangled Representations](https://mlsanity.com/paper/2107.12346/) (2021)
- [Voice Aging with Audio-Visual Style Transfer](https://mlsanity.com/paper/2110.02411/) (2021)
- [Invertible Voice Conversion](https://mlsanity.com/paper/2201.10687/) (2022)
- [Automatic Speech Disentanglement for Voice Conversion using Rank Module and Speech Augmentation](https://mlsanity.com/paper/2306.12259/) (2023)

## Abstract (arXiv)

Although voice conversion (VC) algorithms have achieved remarkable success along with the development of machine learning, superior performance is still difficult to achieve when using nonparallel data. In this paper, we propose using a cycle-consistent adversarial network (CycleGAN) for nonparallel data-based VC training. A CycleGAN is a generative adversarial network (GAN) originally developed for unpaired image-to-image translation. A subjective evaluation of inter-gender conversion demonstrated that the proposed method significantly outperformed a method based on the Merlin open source neural network speech synthesis system (a parallel VC system adapted for our setup) and a GAN-based parallel VC system. This is the first research to show that the performance of a nonparallel VC method can exceed that of state-of-the-art parallel VC methods.