EmoSphere-TTS: Emotional Style and Intensity Modeling via Spherical Emotion Vector for Controllable Emotional Text-to-Speech
<|reference_start|>EmoSphere-TTS: Emotional Style and Intensity Modeling via Spherical Emotion Vector for Controllable Emotional Text-to-Speech: 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.<|reference_end|>
@article{cho2024emosphere-tts:,
    title={EmoSphere-TTS: Emotional Style and Intensity Modeling via Spherical
  Emotion Vector for Controllable Emotional Text-to-Speech},
    author={Deok-Hyeon Cho, Hyung-Seok Oh, Seung-Bin Kim, Sang-Hoon Lee,
  Seong-Whan Lee},
    journal={arXiv preprint arXiv:2406.07803},
    year={2024},
    doi={10.21437/Interspeech.2024-398},
    archivePrefix={arXiv},
    eprint={2406.07803},
    primaryClass={cs.SD cs.AI eess.AS}
}