EMORL-TTS: Reinforcement Learning for Fine-Grained Emotion Control in LLM-based TTS
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917219979493376 |
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| author | Li, Haoxun Liu, Yu Sun, Yuqing Shi, Hanlei Qu, Leyuan Li, Taihao |
| author_facet | Li, Haoxun Liu, Yu Sun, Yuqing Shi, Hanlei Qu, Leyuan Li, Taihao |
| contents | Recent LLM-based TTS systems achieve strong quality and zero-shot ability, but lack fine-grained emotional control due to their reliance on discrete speech tokens. Existing approaches either limit emotions to categorical labels or cannot generalize to LLM-based architectures. We propose EMORL-TTS (Fine-grained Emotion-controllable TTS with Reinforcement Learning), a framework that unifies global intensity control in the VAD space with local emphasis regulation. Our method combines supervised fine-tuning with reinforcement learning guided by task-specific rewards for emotion category, intensity, and emphasis. Moreover, we further investigate how emphasis placement modulates fine-grained emotion intensity. Experiments show that EMORL-TTS improves emotion accuracy, intensity differentiation, and emphasis clarity, while preserving synthesis quality comparable to strong LLM-based baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05758 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EMORL-TTS: Reinforcement Learning for Fine-Grained Emotion Control in LLM-based TTS Li, Haoxun Liu, Yu Sun, Yuqing Shi, Hanlei Qu, Leyuan Li, Taihao Sound Recent LLM-based TTS systems achieve strong quality and zero-shot ability, but lack fine-grained emotional control due to their reliance on discrete speech tokens. Existing approaches either limit emotions to categorical labels or cannot generalize to LLM-based architectures. We propose EMORL-TTS (Fine-grained Emotion-controllable TTS with Reinforcement Learning), a framework that unifies global intensity control in the VAD space with local emphasis regulation. Our method combines supervised fine-tuning with reinforcement learning guided by task-specific rewards for emotion category, intensity, and emphasis. Moreover, we further investigate how emphasis placement modulates fine-grained emotion intensity. Experiments show that EMORL-TTS improves emotion accuracy, intensity differentiation, and emphasis clarity, while preserving synthesis quality comparable to strong LLM-based baselines. |
| title | EMORL-TTS: Reinforcement Learning for Fine-Grained Emotion Control in LLM-based TTS |
| topic | Sound |
| url | https://arxiv.org/abs/2510.05758 |