How Does Pre-trained Wav2Vec 2.0 Perform on Domain-Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications
| Type of publication: | Conference paper |
| Citation: | Juan_SLT2023-2_2023 |
| Publication status: | Accepted |
| Booktitle: | 2023 IEEE Spoken Language Technology Workshop (SLT) |
| Series: | 1 |
| Volume: | 1 |
| Number: | 1 |
| Year: | 2023 |
| Month: | January |
| Organization: | IEEE |
| URL: | https://arxiv.org/abs/2203.168... |
| Abstract: | 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 control (ATC) communications. We benchmark these two models on several open-source and challenging ATC databases with 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 ASR baselines by only fine-tuning E2E acoustic models with a smaller fraction of labeled data. We analyze WERs on the low-resource scenario and gender bias carried by one ATC dataset. |
| Keywords: | air traffic control communications, Automatic Speech Recognition, self-supervised pre-training, wav2vec 2.0 |
| Projects |
Idiap HAAWAII EC H2020- ATCO2 |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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