Exploration of Adapter for Noise Robust Automatic Speech Recognition

arXiv:2402.18275v1 Announce Type: cross
Abstract: 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 …

abstract arxiv asr automatic speech recognition cs.cl cs.sd dataset eess.as exploration networks neural networks noise paper recognition robust speech speech recognition transfer transfer learning type