Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy Speech

Guan-Ting Lin; Wei-Ping Huang; Hung-yi Lee

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arXiv · 2406.11064

Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy Speech

Guan-Ting Lin·Wei-Ping Huang·Hung-yi Lee

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Abstract

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 data, we design a dynamic reset strategy to automatically detect domain shifts and reset the model, making it more effective at handling multi-domain data. 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.

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BibTeX RIS

Guan-Ting Lin, Wei-Ping Huang, Hung-yi Lee. 2024-10-03. Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy Speech. https://arxiv.org/abs/2406.11064

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