Nonparallel Voice Conversion With Augmented Classifier Star Generative Adversarial Networks

Hirokazu Kameoka

doi:10.1109/TASLP.2020.3036784

Nonparallel Voice Conversion With Augmented Classifier Star Generative Adversarial Networks

2020-01-01

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DOI：10.1109/TASLP.2020.3036784

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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 article, 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.

Keywords:

Voice conversion (VC)

nonparallel VC

multi-domain VC

generative adversarial networks (GANs)

CycleGAN

StarGAN

A-StarGAN

Nonparallel Voice Conversion With Augmented Classifier Star Generative Adversarial Networks

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