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.

翻译：适配鲁棒的自动语音识别系统以应对未见噪声场景至关重要。将适配器集成到神经网络中已成为迁移学习的有效技术。本文深入研究了基于适配器的噪声鲁棒语音识别自适应方法。我们使用CHiME-4数据集进行了实验。结果表明，在浅层网络中插入适配器能获得更优效果，且仅在浅层进行适配与跨所有层适配之间无显著差异。此外，模拟数据有助于系统在真实噪声条件下提升性能，但在数据量相同的情况下，真实数据的有效性优于模拟数据。多条件训练对适配器训练仍然有效，而将适配器集成到基于语音增强的语音识别系统中能带来显著改进。