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arXiv · 2505.21198

Universal Speech Enhancement with Regression and Generative Mamba

Rong Chao·Rauf Nasretdinov·Yu-Chiang Frank Wang·Ante Jukić·Szu-Wei Fu·Yu Tsao
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Abstract

The Interspeech 2025 URGENT Challenge aimed to advance universal, robust, and generalizable speech enhancement by unifying speech enhancement tasks across a wide variety of conditions, including seven different distortion types and five languages. We present Universal Speech Enhancement Mamba (USEMamba), a state-space speech enhancement model designed to handle long-range sequence modeling, time-frequency structured processing, and sampling frequency-independent feature extraction. Our approach primarily relies on regression-based modeling, which performs well across most distortions. However, for packet loss and bandwidth extension, where missing content must be inferred, a generative variant of the proposed USEMamba proves more effective. Despite being trained on only a subset of the full training data, USEMamba achieved 2nd place in Track 1 during the blind test phase, demonstrating strong generalization across diverse conditions.

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Rong Chao, Rauf Nasretdinov, Yu-Chiang Frank Wang, Ante Jukić, Szu-Wei Fu, Yu Tsao. 2025-05-27. Universal Speech Enhancement with Regression and Generative Mamba. https://arxiv.org/abs/2505.21198

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