Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy Speech
Guan-Ting Lin, Wei Ping Huang, Hung-yi Lee · 2024
Deep Learning-based end-to-end Automatic Speech Recognition (ASR) has made significant strides but still struggles with performance on out-of-domain samples due to domain shifts in real-world scenarios.Test-Time Adaptation (TTA) methods address this issue by adapting models using test samples at inference time.However, current ASR TTA methods have largely focused on non-continual TTA, which limits cross-sample knowledge learning compared to continual TTA.In this work, we first propose a Fast-slow TTA framework for ASR that leverages the advantage of continual and non-continual TTA.Following this framework, we introduce Dynamic SUTA (DSUTA), an entropy-minimization-based continual TTA method for ASR.To enhance DSUTA robustness for time-varying multi-domain data, we design a dynamic reset strategy to automatically detect domain shifts and reset the model.Our method demonstrates superior performance on various noisy ASR datasets, outperforming both non-continual and continual TTA baselines while maintaining robustness to domain changes without requiring domain boundary information 1 .