How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications. (arXiv:2203.16822v2 [eess.AS] UPDATED)

Recent work on self-supervised pre-training focus on leveraging large-scale
unlabeled speech data to build robust end-to-end (E2E) acoustic models (AM)
that can be later fine-tuned on downstream tasks e.g., automatic speech
recognition (ASR). Yet, few works investigated the impact on performance when
the data properties substantially differ between the pre-training and
fine-tuning phases, termed domain shift. We target this scenario by analyzing
the robustness of Wav2Vec 2.0 and XLS-R models on downstream ASR for a
completely unseen domain, air traffic …

air traffic arxiv asr benchmark communications traffic