Roubst-VC

Toward Degradation-Robust Voice Conversion

• Paper Title: Toward Degradation-Robust Voice Conversion
• Authors: Chien-yu Huang, Kai-Wei Chang, Hung-yi Lee
• Paper Link: https://arxiv.org/abs/2110.07537

To appear in the proceedings of ICASSP 2022, equal contribution from first two authors

Proposed Approaches

Both Speech Enhancement Concatenation and End-to-End Denoising Training can effectively imporve state-of-the-art VC models' degradation robustness and adversarial robustness.

Approach1: Speech Enhancement Concatenation

• Pros: Any-off-the-shelf model applies.
• Cons: More computations are required for inference.

Approach2: End-to-End Denoising Training

• Pros: Combine Voice conversion and speech enhancement in a single model.
• Cons: Need more resouces for training.

Code

• AdaIN-VC
  • AdaIN-VC/: training AdaIN-VC
  • AdaIN-VC-robust/: training AdaIN-VC with data augmentation
  • Reference: https://github.com/cyhuang-tw/AdaIN-VC
• S2VC
  • S2VC/: training S2VC
  • S2VC-robust/: training S2VC with data augmentation
  • Reference: https://github.com/howard1337/S2VC
• assets
  • scripts/: scripts for creating dataset, adding noises, and speech enhancement. For more information, please refer to example usage.
  • VCTK_split.py: train / valid / test dataset split used in the paper.
• Voice-conversion-evaluation
  • objective evaluation, including calculating CER for naturalness evaluation, and SVAR for speaker similarity evaluation
  • reference: https://github.com/tzuhsien/Voice-conversion-evaluation

Demo Page

https://cyhuang-tw.github.io/robust-vc-demo/

Citation

@inproceedings{huang2022toward,
  title={Toward Degradation-Robust Voice Conversion},
  author={Huang, Chien-yu and Chang, Kai-Wei and Lee, Hung-yi},
  booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={6777--6781},
  year={2022},
  organization={IEEE}
}