Non-Parallel Voice Conversion with Augmented Classifier Star Generative Adversarial Networks

This paper proposes a method that allows for non-parallel multi-domain voice conversion (VC) by using a variant of generative adversarial networks (GANs) called StarGAN. The main features of our method, which we term 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 so that it can fully use available training data collected from multiple domains by capturing common latent features that can be shared across different domains. Third, it is able to 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 non-parallel VC task. We also compare them with several baseline methods.