Adapting a robust automatic speech recognition (ASR) system to tackle unseen
noise scenarios is crucial. Integrating adapters into neural networks has
emerged as a potent technique for transfer learning. This paper thoroughly
investigates adapter-based noise-robust ASR adaptation. We conducted the
experiments using the CHiME--4 dataset. The results show that inserting the
adapter in the shallow layer yields superior effectiveness, and there is no
significant difference between adapting solely within the shallow layer and
adapting across all layers. Besides, the simulated data helps the system to
improve its performance under real noise conditions. Nonetheless, when the
amount of data is the same, the real data is more effective than the simulated
data. Multi-condition training remains valid for adapter training. Furthermore,
integrating adapters into speech enhancement-based ASR systems yields
substantial improvements.

应用转移学习，通过将适配器整合到神经网络中，可以有效地处理未知噪声场景，并在浅层插入适配器能够得到更好的效果。实验证明，适配器在浅层和全层适配之间没有明显的差异；在相同数据量下，真实数据比模拟数据更为有效；多条件训练对适配器训练依然有效；将适配器整合到基于语音增强的 ASR 系统中能够大幅度提高性能。