Prosodic phrasing is crucial to the naturalness and intelligibility of
end-to-end Text-to-Speech (TTS). There exist both linguistic and emotional
prosody in natural speech. As the study of prosodic phrasing has been
linguistically motivated, prosodic phrasing for expressive emotion rendering
has not been well studied. In this paper, we propose an emotion-aware prosodic
phrasing model, termed \textit{EmoPP}, to mine the emotional cues of utterance
accurately and predict appropriate phrase breaks. We first conduct objective
observations on the ESD dataset to validate the strong correlation between
emotion and prosodic phrasing. Then the objective and subjective evaluations
show that the EmoPP outperforms all baselines and achieves remarkable
performance in terms of emotion expressiveness. The audio samples and the code
are available at https://github.com/AI-S2-Lab/EmoPP.

为了提高端到端文本转语音（TTS）的自然程度和可理解性，本文提出了一种情感感知的韵律短语模型（EmoPP），准确挖掘话语的情感线索并预测适当的短语断点，并通过客观和主观评估证明 EmoPP 在情感表达能力方面优于所有基线模型，取得了显著的性能。音频样本和代码可在 https://github.com/AI-S2-Lab/EmoPP 获取。