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

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.