Disentangling content and speaking style information is essential for
zero-shot non-parallel voice conversion (VC). Our previous study investigated a
novel framework with disentangled sequential variational autoencoder (DSVAE) as
the backbone for information decomposition. We have demonstrated that
simultaneous disentangling content embedding and speaker embedding from one
utterance is feasible for zero-shot VC. In this study, we continue the
direction by raising one concern about the prior distribution of content branch
in the DSVAE baseline. We find the random initialized prior distribution will
force the content embedding to reduce the phonetic-structure information during
the learning process, which is not a desired property. Here, we seek to achieve
a better content embedding with more phonetic information preserved. We propose
conditional DSVAE, a new model that enables content bias as a condition to the
prior modeling and reshapes the content embedding sampled from the posterior
distribution. In our experiment on the VCTK dataset, we demonstrate that
content embeddings derived from the conditional DSVAE overcome the randomness
and achieve a much better phoneme classification accuracy, a stabilized
vocalization and a better zero-shot VC performance compared with the
competitive DSVAE baseline.

通过条件的 DSVAE 模型，将内容信息的分离，并在语音转换方面实现更好的性能。