通过增强掩码二值性，让语音增强模型在未知环境更稳定。
Test-Time Adaptation For Speech Enhancement Via Mask Polarization
- 用瓦斯赫斯坦距离比较分布，恢复掩码的二峰特性
- 在多种场景下提升性能，且无需额外参数
- 适合边缘设备部署，对资源敏感的语音应用很实用
将语音增强（SE）模型适应到未见环境对实际部署至关重要，但测试时自适应（TTA）在SE领域仍研究不足，主要因缺乏对模型在域偏移下退化机制的理解。我们观察到，基于掩码的SE模型在域偏移下会丧失置信度，预测掩码趋于平滑，失去对语音的明确保留与噪声的有效抑制。基于此洞察，我们提出掩码极化（MPol），一种轻量级TTA方法，通过瓦斯赫斯坦距离进行分布对比，恢复掩码的双峰特性。MPol仅需原训练模型参数，无额外参数，适合资源受限的边缘部署。在多种域偏移和模型架构下的实验表明，MPol实现了稳定且与复杂方法相当的性能提升。
原文摘要 · Abstract (English)
Adapting speech enhancement (SE) models to unseen environments is crucial for practical deployments, yet test-time adaptation (TTA) for SE remains largely under-explored due to a lack of understanding of how SE models degrade under domain shifts. We observe that mask-based SE models lose confidence under domain shifts, with predicted masks becoming flattened and losing decisive speech preservation and noise suppression. Based on this insight, we propose mask polarization (MPol), a lightweight TTA method that restores mask bimodality through distribution comparison using the Wasserstein distance. MPol requires no additional parameters beyond the trained model, making it suitable for resource-constrained edge deployments. Experimental results across diverse domain shifts and architectures demonstrate that MPol achieves very consistent gains that are competitive with significantly more complex approaches.
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