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arXiv · 2510.02187

High-Fidelity Speech Enhancement via Discrete Audio Tokens

Luca A. Lanzendörfer·Frédéric Berdoz·Antonis Asonitis·Roger Wattenhofer
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Abstract

Recent autoregressive transformer-based speech enhancement (SE) methods have shown promising results by leveraging advanced semantic understanding and contextual modeling of speech. However, these approaches often rely on complex multi-stage pipelines and low sampling rate codecs, limiting them to narrow and task-specific speech enhancement. In this work, we introduce DAC-SE1, a simplified language model-based SE framework leveraging discrete high-resolution audio representations; DAC-SE1 preserves fine-grained acoustic details while maintaining semantic coherence. Our experiments show that DAC-SE1 surpasses state-of-the-art autoregressive SE methods on both objective perceptual metrics and in a MUSHRA human evaluation. We release our codebase and model checkpoints to support further research in scalable, unified, and high-quality speech enhancement.

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Luca A. Lanzendörfer, Frédéric Berdoz, Antonis Asonitis, Roger Wattenhofer. 2025-10-02. High-Fidelity Speech Enhancement via Discrete Audio Tokens. https://arxiv.org/abs/2510.02187

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