论文标题
CVC：非平行语音转换的对比度学习
CVC: Contrastive Learning for Non-parallel Voice Conversion
论文作者
论文摘要
循环一致的生成对抗网络（CycleGAN）和基于变异的自动编码器（VAE）模型最近在非平行语音转换中广受欢迎。但是，他们经常遭受艰难的训练过程和不满意的结果。在本文中，我们提出了CVC，这是一种基于对比的学习语音转换方法。与以前的基于自行车的方法相比，CVC仅需要通过利用对比度学习来进行有效的单向GAN训练。当涉及到非并行的一对一语音转换时，CVC处于标准杆或比Cyclegan和VAE更好，同时有效地减少了训练时间。 CVC进一步证明了多对多语音转换的卓越性能，从而实现了看不见的演讲者的转换。
Cycle consistent generative adversarial network (CycleGAN) and variational autoencoder (VAE) based models have gained popularity in non-parallel voice conversion recently. However, they often suffer from difficult training process and unsatisfactory results. In this paper, we propose CVC, a contrastive learning-based adversarial approach for voice conversion. Compared to previous CycleGAN-based methods, CVC only requires an efficient one-way GAN training by taking the advantage of contrastive learning. When it comes to non-parallel one-to-one voice conversion, CVC is on par or better than CycleGAN and VAE while effectively reducing training time. CVC further demonstrates superior performance in many-to-one voice conversion, enabling the conversion from unseen speakers.
