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arXiv · 2609.08204

Stabilizing Instruction Supervision for Instruct-TTS via Controllable Diversification and Drift Filtering

Yizhong Geng·Kecan Mao·Qifei Li·Cong Wang·Yingming Gao·Ruimin Wang·Chunfeng Wang·Hao Li·Ya Li
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Abstract

Instruct-TTS systems expand structured style labels into natural-language training instructions through LLM rewriting, yet we find that over 40% of unconstrained rewrites contain semantic drift that corrupts supervision and weakens generalization. We formalize this problem as instruction supervision instability and propose a data-centric stabilization recipe that jointly improves coverage and fidelity through three mechanisms: controllable instruction diversification for systematic expansion, LLM-based drift filtering for quality control, and attribute-aligned supervision that grounds prosody control in acoustic perturbations. On the Chinese split of InstructTTSEval, our recipe raises instruction-following from 34.5% without fine-tuning and 51.0% with naive fine-tuning to 56.4%, while constrained rewriting reduces drift from 40.4% to 15.4%. Ablations confirm the three mechanisms are complementary, and the drift taxonomy may generalize to instruction-driven generation beyond TTS.

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Yizhong Geng, Kecan Mao, Qifei Li, Cong Wang, Yingming Gao, Ruimin Wang, Chunfeng Wang, Hao Li, Ya Li. 2026-09-08. Stabilizing Instruction Supervision for Instruct-TTS via Controllable Diversification and Drift Filtering. https://arxiv.org/abs/2609.08204

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