  Adapting an automatic speech recognition (ASR) system to unseen noise
environments is crucial. Integrating adapters into neural networks has emerged
as a potent technique for transfer learning. This study thoroughly investigates
adapter-based ASR adaptation in noisy environments. We conducted 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. 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 is still useful for adapter training. Furthermore, integrating
adapters into speech enhancement-based ASR systems yields substantial
improvements.
