  Voice conversion (VC) is a task that transforms the source speaker's timbre,
accent, and tones in audio into another one's while preserving the linguistic
content. It is still a challenging work, especially in a one-shot setting.
Auto-encoder-based VC methods disentangle the speaker and the content in input
speech without given the speaker's identity, so these methods can further
generalize to unseen speakers. The disentangle capability is achieved by vector
quantization (VQ), adversarial training, or instance normalization (IN).
However, the imperfect disentanglement may harm the quality of output speech.
In this work, to further improve audio quality, we use the U-Net architecture
within an auto-encoder-based VC system. We find that to leverage the U-Net
architecture, a strong information bottleneck is necessary. The VQ-based
method, which quantizes the latent vectors, can serve the purpose. The
objective and the subjective evaluations show that the proposed method performs
well in both audio naturalness and speaker similarity.
