Preserving the linguistic content of input speech is essential during voice conversion (VC). The star generative adversarial network-based VC method (StarGAN-VC) is a recently developed method that allows non-parallel many-to-many VC. Although this method is powerful, it can fail to preserve the linguistic content of input speech when the number of available training samples is extremely small. To overcome this problem, we propose the use of automatic speech recognition to assist model training, to improve StarGAN-VC, especially in low-resource scenarios. Experimental results show that using our proposed method, StarGAN-VC can retain more linguistic information than vanilla StarGAN-VC.

翻译：在语音转换期间,必须保留输入语言的语言内容。星体基因对抗网络VC方法(StarGAN-VC)是最近开发的一种方法,允许许多到许多不平行的VC。虽然这种方法很有力,但当现有培训样本数量极小时,它可能无法保留输入语言的语言内容。为了解决这一问题,我们提议使用自动语音识别来协助模式培训,改进StarGAN-VC,特别是在低资源情况下。实验结果表明,使用我们提议的方法,StarGAN-VC可以保留比香草StarGAN-VC更多的语言信息。