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.

本文提出了一种名为 SimSiam-StarGAN-VC 的方法，利用对比学习和高效的同构网络结构解决了 StarGAN-VCs 模型训练过程中存在的挑战，实验结果表明该模型在客观和主观评价指标上都显著优于现有的 StarGAN-VC 方法。