  In this paper, we propose a novel voice conversion strategy to resolve the
mismatch between the training and conversion scenarios when parallel speech
corpus is unavailable for training. Based on auto-encoder and disentanglement
frameworks, we design the proposed model to extract identity and content
representations while reconstructing the input speech signal itself. Since we
use other speaker's identity information in the training process, the training
philosophy is naturally matched with the objective of voice conversion process.
In addition, we effectively design the disentanglement framework to reliably
preserve linguistic information and to enhance the quality of converted speech
signals. The superiority of the proposed method is shown in subjective
listening tests as well as objective measures.

Audio and Speech Processing