Post-Training Zero-Shot TTS for Fine-Grained Emotion and Duration Control via Natural Language

Abstract

Audiobook narration, conversational agents, and audiovisual dubbing require fine-grained control over emotion and pacing within an utterance, yet many text-to-speech (TTS) systems remain limited to utterance-level conditioning. We propose a unified post-training framework for natural-language segment-level emotion and duration control. Supervised fine-tuning (SFT) establishes instruction following, while group relative policy optimization (GRPO) further improves control accuracy using emotion, duration, content, and speaker rewards. Without modifying the pretrained architecture or adding inference-time control modules, our method improves fine-grained controllability while preserving intelligibility and speaker identity, demonstrating post-training as an effective way to extend pretrained TTS models.