2022
DOI: 10.48550/arxiv.2207.00756
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Learning Noise-independent Speech Representation for High-quality Voice Conversion for Noisy Target Speakers

Abstract: Building a voice conversion system for noisy target speakers, such as users providing noisy samples or Internet found data, is a challenging task since the use of contaminated speech in model training will apparently degrade the conversion performance. In this paper, we leverage the advances of our recently proposed Glow-WaveGAN [1] and propose a noise-independent speech representation learning approach for high-quality voice conversion for noisy target speakers. Specifically, we learn a latent feature space w… Show more

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