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arXiv · 2608.20387

Poly-InstructTTS: Learning In-the-Wild Expressive Speech Synthesis from Open-Ended Instructions

Junhui Zhang·Qianhui Xu·Qingxiang Guo·Dawei Yang·Ling Miao·Qiangqiang Wang·Yang Song
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Abstract

While recent text-to-speech (TTS) models achieve high naturalness, controlling fine-grained expression via natural-language instructions remains challenging. We introduce Poly- InstructTTS, which learns expressive speech from open-ended instructions using in-the-wild audiovisual data. We build a scalable multi-modal pipeline to construct a 1,000-hour instruction-annotated corpus covering 1,000+ fine-grained emotions and styles. The framework uses a prompt-free GPT with attribute-based thinking tokens, followed by a flow-matching module that injects timbre from a reference audio. We also present a speaker fine-tuning procedure to transfer instruction control to specific speakers while preserving persona. We further extend InstructTTSEval with broader tasks. Experiments show that Poly-InstructTTS delivers strong performance in instruction adherence and expressiveness. Audio demos and the expanded testset are available on our project page.

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Junhui Zhang, Qianhui Xu, Qingxiang Guo, Dawei Yang, Ling Miao, Qiangqiang Wang, Yang Song. 2026-06-30. Poly-InstructTTS: Learning In-the-Wild Expressive Speech Synthesis from Open-Ended Instructions. https://arxiv.org/abs/2608.20387

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