Despite rapid advances in the field of emotional text-to-speech (TTS), recent
studies primarily focus on mimicking the average style of a particular emotion.
As a result, the ability to manipulate speech emotion remains constrained to
several predefined labels, compromising the ability to reflect the nuanced
variations of emotion. In this paper, we propose EmoSphere-TTS, which
synthesizes expressive emotional speech by using a spherical emotion vector to
control the emotional style and intensity of the synthetic speech. Without any
human annotation, we use the arousal, valence, and dominance pseudo-labels to
model the complex nature of emotion via a Cartesian-spherical transformation.
Furthermore, we propose a dual conditional adversarial network to improve the
quality of generated speech by reflecting the multi-aspect characteristics. The
experimental results demonstrate the model ability to control emotional style
and intensity with high-quality expressive speech.

通过使用球形情感向量来控制合成语音的情感风格和强度，提出了 EmoSphere-TTS，该系统实现了表达性的情感语音。通过使用唤起度、愉悦度和支配性伪标签来模拟情感的复杂特性，进而提出了双条件对抗网络来提高生成语音质量，实验结果表明该模型能够高质量地控制情感风格和强度。