We often verbally express emotions in a multifaceted manner, they may vary in their intensities and may be expressed not just as a single but as a mixture of emotions. This wide spectrum of emotions is well-studied in the structural model of emotions, which represents variety of emotions as derivative products of primary emotions with varying degrees of intensity. In this paper, we propose an emotional text-to-speech design to simulate a wider spectrum of emotions grounded on the structural model. Our proposed design, Daisy-TTS, incorporates a prosody encoder to learn emotionally-separable prosody embedding as a proxy for emotion. This emotion representation allows the model to simulate: (1) Primary emotions, as learned from the training samples, (2) Secondary emotions, as a mixture of primary emotions, (3) Intensity-level, by scaling the emotion embedding, and (4) Emotions polarity, by negating the emotion embedding. Through a series of perceptual evaluations, Daisy-TTS demonstrated overall higher emotional speech naturalness and emotion perceiveability compared to the baseline.

翻译：我们通常以多层面的方式口头表达情感，这些情感可能在强度上有所不同，并且可能不仅仅是单一情感，而是多种情感的混合。这种广泛的情感频谱在情感结构模型中得到了深入研究，该模型将各种情感表示为不同强度基本情感的衍生产物。在本文中，我们提出了一种基于情感结构模型的、旨在模拟更广泛情感频谱的情感文本转语音设计方案。我们提出的设计Daisy-TTS，包含一个韵律编码器，用于学习情感可分离的韵律嵌入作为情感的代理表示。这种情感表示使模型能够模拟：（1）从训练样本中学到的基本情感，（2）作为基本情感混合的次级情感，（3）通过缩放情感嵌入实现的强度级别，以及（4）通过取反情感嵌入实现的情感极性。通过一系列感知评估，与基线模型相比，Daisy-TTS在情感语音自然度和情感可感知性方面总体上表现出更高的水平。