  Recently, there has been an increasing interest in neural speech synthesis.
While the deep neural network achieves the state-of-the-art result in
text-to-speech (TTS) tasks, how to generate a more emotional and more
expressive speech is becoming a new challenge to researchers due to the
scarcity of high-quality emotion speech dataset and the lack of advanced
emotional TTS model. In this paper, we first briefly introduce and publicly
release a Mandarin emotion speech dataset including 9,724 samples with audio
files and its emotion human-labeled annotation. After that, we propose a simple
but efficient architecture for emotional speech synthesis called EMSpeech.
Unlike those models which need additional reference audio as input, our model
could predict emotion labels just from the input text and generate more
expressive speech conditioned on the emotion embedding. In the experiment
phase, we first validate the effectiveness of our dataset by an emotion
classification task. Then we train our model on the proposed dataset and
conduct a series of subjective evaluations. Finally, by showing a comparable
performance in the emotional speech synthesis task, we successfully demonstrate
the ability of the proposed model.
