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

It matters because Supervised Fine-Tuning often converges to statistically averaged prosody that limits emotional expressiveness in LLM-based Text-to-Speech models.

Recently, Large Language Model (LLM)-based Text-to-Speech (TTS) models have achieved remarkable naturalness. However, the standard Supervised Fine-Tuning paradigm often converges to statistically averaged prosody, limiting emotional expressiveness. While preference-driven…