Emotion embedding space learned from references is a straightforward approach
for emotion transfer in encoder-decoder structured emotional text to speech
(TTS) systems. However, the transferred emotion in the synthetic speech is not
accurate and expressive enough with emotion category confusions. Moreover, it
is hard to select an appropriate reference to deliver desired emotion strength.
To solve these problems, we propose a novel approach based on Tacotron. First,
we plug two emotion classifiers -- one after the reference encoder, one after
the decoder output -- to enhance the emotion-discriminative ability of the
emotion embedding and the predicted mel-spectrum. Second, we adopt style loss
to measure the difference between the generated and reference mel-spectrum. The
emotion strength in the synthetic speech can be controlled by adjusting the
value of the emotion embedding as the emotion embedding can be viewed as the
feature map of the mel-spectrum. Experiments on emotion transfer and strength
control have shown that the synthetic speech of the proposed method is more
accurate and expressive with less emotion category confusions and the control
of emotion strength is more salient to listeners.

提出了一种基于 Tacotron 的新方法，通过引入情感分类器和样式损失，增强情感嵌入的区分能力，并通过控制情感嵌入的值，控制合成语音中的情感强度。实验结果表明，该方法合成的语音具有更高的准确性和表现力，并且情感强度的控制更加显著。