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.

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}
}