# Noise-robust voice conversion with domain adversarial training

arXiv: https://arxiv.org/abs/2201.10693 · HTML: https://mlsanity.com/paper/2201.10693/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2022.

## Built on by (most cited first, 5 of 5)

- [REF-VC: Robust, Expressive and Fast Zero-Shot Voice Conversion with Diffusion Transformers](https://mlsanity.com/paper/2508.04996/) (2025)
- [Towards the Transferable Audio Adversarial Attack via Ensemble Methods](https://mlsanity.com/paper/2304.08811/) (2023)
- [Noro: Noise-Robust One-shot Voice Conversion with Hidden Speaker Representation Learning](https://mlsanity.com/paper/2411.19770/) (2024)
- [R2-SVC: Towards Real-World Robust and Expressive Zero-shot Singing Voice Conversion](https://mlsanity.com/paper/2510.20677/) (2025)
- [Degrading Voice: A Comprehensive Overview of Robust Voice Conversion Through Input Manipulation](https://mlsanity.com/paper/2512.06304/) (2025)

## Abstract (arXiv)

Voice conversion has made great progress in the past few years under the studio-quality test scenario in terms of speech quality and speaker similarity. However, in real applications, test speech from source speaker or target speaker can be corrupted by various environment noises, which seriously degrade the speech quality and speaker similarity. In this paper, we propose a novel encoder-decoder based noise-robust voice conversion framework, which consists of a speaker encoder, a content encoder, a decoder, and two domain adversarial neural networks. Specifically, we integrate disentangling speaker and content representation technique with domain adversarial training technique. Domain adversarial training makes speaker representations and content representations extracted by speaker encoder and content encoder from clean speech and noisy speech in the same space, respectively. In this way, the learned speaker and content representations are noise-invariant. Therefore, the two noise-invariant representations can be taken as input by the decoder to predict the clean converted spectrum. The experimental results demonstrate that our proposed method can synthesize clean converted speech under noisy test scenarios, where the source speech and target speech can be corrupted by seen or unseen noise types during the training process. Additionally, both speech quality and speaker similarity are improved.