# EMORL-TTS: Reinforcement Learning for Fine-Grained Emotion Control in LLM-based TTS

arXiv: https://arxiv.org/abs/2510.05758 · HTML: https://mlsanity.com/paper/2510.05758/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2025.

## Built on by (most cited first, 4 of 4)

- [EmoTra-TTS: Smooth Intra-Utterance Emotion Transitions for Speech Synthesis](https://mlsanity.com/paper/2608.23791/) (2026)
- [HPRO: Hierarchical Progressive Reward Optimization via Preference Extraction for Emotional Text-to-Speech](https://mlsanity.com/paper/2606.28249/) (2026)
- [Diagnose, Then Refine: A Closed-Loop TTS System with AudioLLM-Guided Correction](https://mlsanity.com/paper/2608.28970/) (2026)
- [Tracing a Sparse Emotion-Control Circuit in LLM-Based Text-to-Speech](https://mlsanity.com/paper/2610.05080/) (2026)

## Abstract (arXiv)

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.