  This paper proposes a simple and robust zero-shot voice conversion system
with a cycle structure and mel-spectrogram pre-processing. Previous works
suffer from information loss and poor synthesis quality due to their reliance
on a carefully designed bottleneck structure. Moreover, models relying solely
on self-reconstruction loss struggled with reproducing different speakers'
voices. To address these issues, we suggested a cycle-consistency loss that
considers conversion back and forth between target and source speakers.
Additionally, stacked random-shuffled mel-spectrograms and a label smoothing
method are utilized during speaker encoder training to extract a
time-independent global speaker representation from speech, which is the key to
a zero-shot conversion. Our model outperforms existing state-of-the-art results
in both subjective and objective evaluations. Furthermore, it facilitates
cross-lingual voice conversions and enhances the quality of synthesized speech.
