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.

提出了一个简单而强大的零样本语音转换系统，使用了循环结构和梅尔频谱预处理，通过循环一致性损失考虑目标和源说话者之间的转换，同时利用堆叠的随机排序梅尔频谱和标签平滑方法在说话者编码器训练过程中提取不受时间约束的全局说话者表示，模型在主观和客观评估中表现优于现有的最先进结果，并可促进跨语言语音转换并提高合成语音的质量。