Dynamically Slimmable Speech Enhancement Network with Metric-Guided Training

Haixin Zhao; Kaixuan Yang; Nilesh Madhu

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arXiv · 2510.11395

Dynamically Slimmable Speech Enhancement Network with Metric-Guided Training

Haixin Zhao·Kaixuan Yang·Nilesh Madhu

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Abstract

To further reduce the complexity of lightweight speech enhancement models, we introduce a gating-based Dynamically Slimmable Network (DSN). The DSN comprises static and dynamic components. For architecture-independent applicability, we introduce distinct dynamic structures targeting the commonly used components, namely, grouped recurrent neural network units, multi-head attention, convolutional, and fully connected layers. A policy module adaptively governs the use of dynamic parts at a frame-wise resolution according to the input signal quality, controlling computational load. We further propose Metric-Guided Training (MGT) to explicitly guide the policy module in assessing input speech quality. Experimental results demonstrate that the DSN achieves comparable enhancement performance in instrumental metrics to the state-of-the-art lightweight baseline, while using only 73% of its computational load on average. Evaluations of dynamic component usage ratios indicate that the MGT-DSN can appropriately allocate network resources according to the severity of input signal distortion.

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BibTeX RIS

Haixin Zhao, Kaixuan Yang, Nilesh Madhu. 2026-03-13. Dynamically Slimmable Speech Enhancement Network with Metric-Guided Training. https://arxiv.org/abs/2510.11395

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