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arXiv · 2609.22824

Adaptive Depth and Expert Refinement for Efficient Speech Enhancement

Xikun Lu·Yujian Ma·Yunda Chen·Xianquan Jiang·Jinqiu Sang
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Abstract

Most neural speech enhancement systems use a fixed processing depth for all inputs, which can introduce unnecessary computation when fewer refinement steps are sufficient. We propose Adaptive Depth and Expert Refinement (ADER), a parameter-shared progressive enhancement framework with input-dependent computation. ADER combines an Adaptive Depth Controller (ADC) for hard early termination with a Conditional Expert Router (CER) that selects one lightweight residual adapter at each executed refinement iteration. We further introduce Exit-aware Intermediate Supervision (EIS) to directly optimize candidate intermediate outputs for early exit. On VCTK-DEMAND, ADER reduces the parameter count and average computation of MP-SENet by 70.4% and 51.3%, respectively, while achieving a WB-PESQ of 3.37. Overall, ADER enables input-dependent refinement and reduces redundant computation during inference.

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Xikun Lu, Yujian Ma, Yunda Chen, Xianquan Jiang, Jinqiu Sang. 2026-09-19. Adaptive Depth and Expert Refinement for Efficient Speech Enhancement. https://arxiv.org/abs/2609.22824

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