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Nowadays, as more and more systems achieve good performance in traditionalvoice conversion (VC) tasks, people's attention gradually turns to VC tasksunder extreme conditions. In this paper, we propose a novel method forzero-shot voice conversion. We aim to obtain intermediate representations forspeaker-content disentanglement of speech to better remove speaker informationand get pure content information. Accordingly, our proposed framework containsa module that removes the speaker information from the acoustic feature of thesource speaker. Moreover, speaker information control is added to our system tomaintain the voice cloning performance. The proposed system is evaluated bysubjective and objective metrics. Results show that our proposed systemsignificantly reduces the trade-off problem in zero-shot voice conversion,while it also manages to have high spoofing power to the speaker verificationsystem.

SIG-VC: A Speaker Information Guided Zero-shot Voice Conversion System for Both Human Beings and Machines