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arXiv · 2607.17900

Harness TTS: Towards Context-Aware Expressive Speech Synthesis with Harness Layer

Shengfan Shen·Di Wu·Xingchen Song·Dinghao Zhou·Pengyu Cheng·Sixiang Lyu·Jian Luan·Shuai Wang
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Abstract

Expressive speech synthesis for voice assistants requires flexible style control that adapts to explicit requests and broader interaction context. We propose Harness TTS, a lightweight control layer that wraps around a TTS engine to externalize and govern its expressive behavior. It reformulates style control as closed-set prompt-tool routing: offline, a compact registry of stylistic prompt tools is constructed with structured metadata; online, an LLM planner selects the appropriate tool based on a priority-aware observation schema, and the TTS executor synthesizes speech using the corresponding prompt audio. We evaluate Harness TTS on both routing and synthesis tasks. In routing, Qwen3-4B achieves Top-1 accuracies of 74.3%, 43.0%, and 64.6% on explicit, implicit, and conflict subsets. For synthesis, experiments on CosyVoice3 and VoxCPM2 show that Harness TTS outperforms instruction-only control, achieving higher instruction-following win rates (margins of 23.1-35.6 points on CosyVoice3 and 13.8-20.0 points on VoxCPM2) and improving UTMOSv2 scores by 0.11-0.38. Moreover, the 4B planner delivers its first tool recommendation in under 50 ms in standard mode, introducing negligible latency for real-time interaction. These results demonstrate that equipping TTS engines with a dedicated Harness layer offers a practical, auditable, and context-aware solution for voice assistant expression control.

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Shengfan Shen, Di Wu, Xingchen Song, Dinghao Zhou, Pengyu Cheng, Sixiang Lyu, Jian Luan, Shuai Wang. 2026-07-21. Harness TTS: Towards Context-Aware Expressive Speech Synthesis with Harness Layer. https://arxiv.org/abs/2607.17900

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