  This paper proposes an end-to-end emotional speech synthesis (ESS) method
which adopts global style tokens (GSTs) for semi-supervised training. This
model is built based on the GST-Tacotron framework. The style tokens are
defined to present emotion categories. A cross entropy loss function between
token weights and emotion labels is designed to obtain the interpretability of
style tokens utilizing the small portion of training data with emotion labels.
Emotion recognition experiments confirm that this method can achieve one-to-one
correspondence between style tokens and emotion categories effectively.
Objective and subjective evaluation results show that our model outperforms the
conventional Tacotron model for ESS when only 5\% of training data has emotion
labels. Its subjective performance is close to the Tacotron model trained using
all emotion labels.
