  We previously proposed a method that allows for nonparallel voice conversion
(VC) by using a variant of generative adversarial networks (GANs) called
StarGAN. The main features of our method, called StarGAN-VC, are as follows:
First, it requires no parallel utterances, transcriptions, or time alignment
procedures for speech generator training. Second, it can simultaneously learn
mappings across multiple domains using a single generator network and thus
fully exploit available training data collected from multiple domains to
capture latent features that are common to all the domains. Third, it can
generate converted speech signals quickly enough to allow real-time
implementations and requires only several minutes of training examples to
generate reasonably realistic-sounding speech. In this paper, we describe three
formulations of StarGAN, including a newly introduced novel StarGAN variant
called "Augmented classifier StarGAN (A-StarGAN)", and compare them in a
nonparallel VC task. We also compare them with several baseline methods.
