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New Speech-Rewarded Style Planning enhances TTS control

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called Speech-Rewarded Style Planning (SRSP) to improve controllable text-to-speech (TTS) systems. Unlike previous approaches that rely on text descriptions of speech style, SRSP trains a style planner using a frozen TTS model. This planner generates instructions that are optimized using group-relative policy optimization (GRPO) with the likelihood of target speech tokens as the reward. Experiments on the ISCSLP 2026 CoT-TTS corpus showed that SRSP outperforms baseline methods in terms of speech-style similarity, emotion similarity, and mel-cepstral distortion, while also demonstrating improved contextual appropriateness and reference consistency in expressive speech evaluations. AI

IMPACT Improves control and naturalness in text-to-speech systems, potentially leading to more expressive and contextually appropriate synthesized speech.

RANK_REASON The cluster contains a research paper detailing a new method for text-to-speech synthesis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Speech-Rewarded Style Planning enhances TTS control

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The cluster contains a research paper detailing a new method for text-to-speech synthesis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Shiao Zhu, Lianbo Liu, Sizhen Lyu, Yuzhe Wang, Sheng Li, Takahiro Shinozaki ·

    Beyond Speech Captions: Speech-Rewarded Style Planning for Conversational Text-to-Speech

    arXiv:2610.11461v1 Announce Type: cross Abstract: Natural-language style descriptions provide an interpretable interface between large language models (LLMs) and controllable text-to-speech (TTS). However, using descriptions as pseudo-labels compresses target acoustics into text,…

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