Investigation of Adapter for Automatic Speech Recognition in Noisy Environment
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要