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- W2805669069 abstract "This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN. Our method, which we call StarGAN-VC, is noteworthy in that it (1) requires no parallel utterances, transcriptions, or 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 converted speech signals quickly enough to allow 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." @default.
- W2805669069 created "2018-06-13" @default.
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- W2805669069 date "2018-06-06" @default.
- W2805669069 modified "2023-10-18" @default.
- W2805669069 title "StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks" @default.
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- W2805669069 doi "https://doi.org/10.48550/arxiv.1806.02169" @default.
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