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arXiv · 2610.34814

TIMUSE: Text-Initialized Masked Semantic Token Modeling for Universal Speech Enhancement

Yiwei Guo·Hao Qiu·Mohan Ye·Zhijie Deng·Zehua Chen·Kai Yu
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Abstract

Discrete-token-based universal speech enhancement (USE) enables strong sequence modeling but often suffers from a quality--fidelity trade-off, where perceptually plausible outputs may alter linguistic content or speaker characteristics. We present TIMUSE, a two-stage USE framework combining text-initialized masked semantic restoration with acoustic reconstruction. A pretrained diffusion language model (DLM) is adapted with LoRA to recover clean semantic tokens using masked generative modeling, while a conditional flow matching model reconstructs clean 24\,kHz speech from the recovered semantic tokens and original degraded speech. Both system-level and controlled comparisons show that TIMUSE achieves a better balance between perceptual quality and fidelity than existing discrete USE models. Ablations further show that text DLM initialization brings a targeted gain under severe degradations.

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Yiwei Guo, Hao Qiu, Mohan Ye, Zhijie Deng, Zehua Chen, Kai Yu. 2026-10-26. TIMUSE: Text-Initialized Masked Semantic Token Modeling for Universal Speech Enhancement. https://arxiv.org/abs/2610.34814

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