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

Sound;Artificial Intelligence;Audio and Speech Processing