HPRO

HPRO: Hierarchical Progressive Reward Optimization for Emotional Text-to-Speech

Researchers propose HPRO, a hierarchical progressive reward optimization framework for LLM-based Text-to-Speech systems that improves emotional expressiveness. The approach introduces HD-Emo codec as a differentiable reward model that separates content and style preference tokens to avoid conflicting gradients, and bridges sentence-level and frame-level reward signals through progressive multi-scale alignment. Experiments show improved emotional expressiveness while preserving linguistic intelligibility.