SRSP trains a style planner with a frozen TTS model's speech-likelihood reward, beating captioning baselines that see the target audio on five acoustic metrics
The work first shows that speech-text alignment only weakly predicts downstream acoustic similarity, then proposes Speech-Rewarded Style Planning (SRSP), which trains a text-based style planner with GRPO using the teacher-forced likelihood of target speech tokens under a frozen TTS model as reward; on an English subset of the ISCSLP 2026 CoT-TTS corpus, SRSP outperforms the Base LLM and target-audio-informed captioning baselines on five downstream acoustic metrics and gains in LLM-judged contextual appropriateness and reference consistency.