Boosting Star-GANs for Voice Conversion with Contrastive Discriminator

The overall architecture of SimSiam-StarGAN-VC


Nonparallel multi-domain voice conversion methods such as the StarGAN-VCs have been widely applied in many scenarios. However, the training of these models usually poses a challenge due to their complicated adversarial network architectures. To address this, in this work we leverage the state-of-the-art contrastive learning techniques and incorporate an efficient Siamese network structure into the StarGAN discriminator. Our method is called SimSiam-StarGAN-VC and it boosts the training stability and effectively prevents the discriminator overfitting issue in the training process. We conduct experiments on the Voice Conversion Challenge (VCC 2018) dataset, plus a user study to validate the performance of our framework. Our experimental results show that SimSiam-StarGAN-VC significantly outperforms existing StarGAN-VC methods in terms of both the objective and subjective metrics.

In International Conference on Neural Information Processing
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Shijing Si
Shijing Si
Ning Cheng
Ning Cheng