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Recent work on self-supervised pre-training focus on leveraging large-scaleunlabeled speech data to build robust end-to-end (E2E) acoustic models (AM)that can be later fine-tuned on downstream tasks e.g., automatic speechrecognition (ASR). Yet, few works investigated the impact on performance whenthe data properties substantially differ between the pre-training andfine-tuning phases, termed domain shift. We target this scenario by analyzingthe robustness of Wav2Vec 2.0 and XLS-R models on downstream ASR for acompletely unseen domain, air traffic control (ATC) communications. Webenchmark these two models on several open-source and challenging ATC databaseswith signal-to-noise ratio between 5 and 20 dB. Relative word error rate (WER)reductions between 20% to 40% are obtained in comparison to hybrid-based ASRbaselines by only fine-tuning E2E acoustic models with a smaller fraction oflabeled data. We analyze WERs on the low-resource scenario and gender biascarried by one ATC dataset.

How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications