EmoSphere-TTS: Emotional Style and Intensity Modeling via Spherical Emotion Vector for Controllable Emotional Text-to-Speech

• URL: http://arxiv.org/abs/2406.07803v1
• Date: Wed, 12 Jun 2024 01:40:29 GMT
• Title: EmoSphere-TTS: Emotional Style and Intensity Modeling via Spherical Emotion Vector for Controllable Emotional Text-to-Speech
• Authors: Deok-Hyeon Cho, Hyung-Seok Oh, Seung-Bin Kim, Sang-Hoon Lee, Seong-Whan Lee,
• Abstract summary: EmoSphere-TTS synthesizes expressive emotional speech by using a spherical emotion vector to control the emotional style and intensity of the synthetic speech. We propose a dual conditional adversarial network to improve the quality of generated speech by reflecting the multi-aspect characteristics.
• Score: 34.03787613163788
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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