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.

本篇论文提供了包括 9,724 个样本的情感语音数据集，并提出了一种名为 EMSpeech 的情感语音合成模型，该模型无需参考音频即可从文本预测情感标签并生成更富表现力的语音。在实验中，作者验证了数据集的有效性，证明了该模型在情感语音合成任务中取得了显著性能。