StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
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This paper proposes a method that allows for non-parallel many-to-many voice conversion (VC) by using a variant of generative adversarial networks (GANs) called StarGAN. Our method, which we term StarGAN-VC, is remarkable in that it (1) requires neither parallel utterances, transcriptions, nor time alignment procedures for speech generator training, (2) simultaneously learns many-to-many mappings across different attribute domains using a single generator network, (3) is able to generate signals of converted speech quickly enough to allow for real-time implementations and (4) requires only several minutes of training examples to generate reasonably realistic-sounding speech. Subjective evaluation experiments on a non-parallel many-to-many speaker identity conversion task revealed that the proposed method obtained higher sound quality and speaker similarity than a state-of-the-art method based on variational autoencoding GANs.
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