  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.
