  Non-parallel many-to-many voice conversion remains an interesting but
challenging speech processing task. Many style-transfer-inspired methods such
as generative adversarial networks (GANs) and variational autoencoders (VAEs)
have been proposed. Recently, AutoVC, a conditional autoencoders (CAEs) based
method achieved state-of-the-art results by disentangling the speaker identity
and speech content using information-constraining bottlenecks, and it achieves
zero-shot conversion by swapping in a different speaker's identity embedding to
synthesize a new voice. However, we found that while speaker identity is
disentangled from speech content, a significant amount of prosodic information,
such as source F0, leaks through the bottleneck, causing target F0 to fluctuate
unnaturally. Furthermore, AutoVC has no control of the converted F0 and thus
unsuitable for many applications. In the paper, we modified and improved
autoencoder-based voice conversion to disentangle content, F0, and speaker
identity at the same time. Therefore, we can control the F0 contour, generate
speech with F0 consistent with the target speaker, and significantly improve
quality and similarity. We support our improvement through quantitative and
qualitative analysis.
