  Despite the remarkable progress made in synthesizing emotional speech from
text, it is still challenging to provide emotion information to existing speech
segments. Previous methods mainly rely on parallel data, and few works have
studied the generalization ability for one model to transfer emotion
information across different languages. To cope with such problems, we propose
an emotion transfer system named ET-GAN, for learning language-independent
emotion transfer from one emotion to another without parallel training samples.
Based on cycle-consistent generative adversarial network, our method ensures
the transfer of only emotion information across speeches with simple loss
designs. Besides, we introduce an approach for migrating emotion information
across different languages by using transfer learning. The experiment results
show that our method can efficiently generate high-quality emotional speech for
any given emotion category, without aligned speech pairs.
