Robust Disentangled Variational Speech Representation Learning for Zero-Shot Voice Conversion

Traditional studies on voice conversion (VC) have made progress with parallel training data and known speakers. Good voice conversion quality is obtained by exploring better alignment modules or expressive mapping functions. In this study, we investigate zero-shot VC from a novel perspective of self...

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Vydané v:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 6572 - 6576
Hlavní autori:	Lian, Jiachen, Zhang, Chunlei, Yu, Dong
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.01.2022
Predmet:	Acoustics Representation learning Self-supervised Disentangled representation learning Speech recognition System performance Training Training data variational autoencoder Vocoders voice conversion zero-shot style transfer
ISSN:	2379-190X
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