  Voice conversion technologies have been greatly improved in recent years with
the help of deep learning, but their capabilities of producing natural sounding
utterances in different conditions remain unclear. In this paper, we gave a
thorough study of the robustness of known VC models. We also modified these
models, such as the replacement of speaker embeddings, to further improve their
performances. We found that the sampling rate and audio duration greatly
influence voice conversion. All the VC models suffer from unseen data, but
AdaIN-VC is relatively more robust. Also, the speaker embedding jointly trained
is more suitable for voice conversion than those trained on speaker
identification.
