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This paper proposes a simple and robust zero-shot voice conversion systemwith a cycle structure and mel-spectrogram pre-processing. Previous workssuffer from information loss and poor synthesis quality due to their relianceon a carefully designed bottleneck structure. Moreover, models relying solelyon self-reconstruction loss struggled with reproducing different speakers'voices. To address these issues, we suggested a cycle-consistency loss thatconsiders conversion back and forth between target and source speakers.Additionally, stacked random-shuffled mel-spectrograms and a label smoothingmethod are utilized during speaker encoder training to extract atime-independent global speaker representation from speech, which is the key toa zero-shot conversion. Our model outperforms existing state-of-the-art resultsin both subjective and objective evaluations. Furthermore, it facilitatescross-lingual voice conversions and enhances the quality of synthesized speech.

AutoCycle-VC: Towards Bottleneck-Independent Zero-Shot Cross-Lingual Voice Conversion