# ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications arXiv: https://arxiv.org/abs/2211.04054 ยท HTML: https://mlsanity.com/paper/2211.04054/ ## Key facts - Cited by 19 papers in the map (data through 14 Oct 2026). - Builds on 22 of its 80 references that are in the map. - First posted on arXiv 8 Nov 2022. ## Builds on (most cited first, 22 of 22) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019) - [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016) - [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020) - [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019) - [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021) - [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018) - [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019) - [Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks](https://mlsanity.com/paper/2012.14952/) (2020) - [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://mlsanity.com/paper/2203.16822/) (2022) - [Automatic Speech Recognition Benchmark for Air-Traffic Communications](https://mlsanity.com/paper/2006.10304/) (2020) - [The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detection](https://mlsanity.com/paper/1810.12614/) (2018) - [BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications](https://mlsanity.com/paper/2110.05781/) (2021) - [A Survey on Recent Advances in Sequence Labeling from Deep Learning Models](https://mlsanity.com/paper/2011.06727/) (2020) - [Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems](https://mlsanity.com/paper/2104.03643/) (2021) - [Improving callsign recognition with air-surveillance data in air-traffic communication](https://mlsanity.com/paper/2108.12156/) (2021) - [What do we Really Know about State of the Art NER?](https://mlsanity.com/paper/2205.00034/) (2022) - [A two-step approach to leverage contextual data: speech recognition in air-traffic communications](https://mlsanity.com/paper/2202.03725/) (2022) - [Detecting English Speech in the Air Traffic Control Voice Communication](https://mlsanity.com/paper/2104.02332/) (2021) - [Comparing supervised and self-supervised embedding for ExVo Multi-Task learning track](https://mlsanity.com/paper/2206.11968/) (2022) - [Call-sign recognition and understanding for noisy air-traffic transcripts using surveillance information](https://mlsanity.com/paper/2204.06309/) (2022) ## Built on by (most cited first, 19 of 19) - [TartanAviation: Image, Speech, and ADS-B Trajectory Datasets for Terminal Airspace Operations](https://mlsanity.com/paper/2403.03372/) (2024) - [Implementing contextual biasing in GPU decoder for online ASR](https://mlsanity.com/paper/2306.15685/) (2023) - [A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers](https://mlsanity.com/paper/2304.07842/) (2023) - [Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications](https://mlsanity.com/paper/2502.20311/) (2025) - [Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator](https://mlsanity.com/paper/2212.07164/) (2022) - [BEST-RQ-Based Self-Supervised Learning for Whisper Domain Adaptation](https://mlsanity.com/paper/2510.24570/) (2025) - [Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understanding](https://mlsanity.com/paper/2305.01155/) (2023) - [On the N-gram Approximation of Pre-trained Language Models](https://mlsanity.com/paper/2306.06892/) (2023) - [In-domain SSL pre-training and streaming ASR](https://mlsanity.com/paper/2509.12101/) (2025) - [Air Traffic Control Using Large Language Models: Prompt Engineering, Architecture, and Evaluation](https://mlsanity.com/paper/2608.19299/) (2026) - [A Modular, Data-Free Pipeline for Multi-Label Intention Recognition in Transportation Agentic AI Applications](https://mlsanity.com/paper/2511.03363/) (2025) - [RECOVER: Robust Entity Correction via agentic Orchestration of hypothesis Variants for Evidence-based Recovery](https://mlsanity.com/paper/2603.16411/) (2026) - [SCOPE: A Lightweight-training LLM Framework for Air Traffic Control Readback Monitoring](https://mlsanity.com/paper/2605.29543/) (2026) - [MoDiCoL: A Modular Diagnostic Continual Learning Dataset for Robust Speech Recognition](https://mlsanity.com/paper/2606.14459/) (2026) - [ASTRA: A Scalable Next-Generation ATCO Training Simulator with Autonomous Simpilots](https://mlsanity.com/paper/2606.18319/) (2026) - [Synthetic Audio Generation Framework for Air Traffic Control Speech Recognition](https://mlsanity.com/paper/2606.21340/) (2026) - [Beyond Semantic Accuracy: Consequence-Aware Evaluation for Safety-Critical Language Understanding](https://mlsanity.com/paper/2608.24621/) (2026) - [Formal, Executable and Explainable Runtime Monitoring of Spoken Air Traffic Control Operational Procedures](https://mlsanity.com/paper/2608.25926/) (2026) - [V2TATC: Joint Voice-Trajectory Embedding and Dataset for Air Traffic Controller Situational Awareness](https://mlsanity.com/paper/2608.28981/) (2026) ## Abstract (arXiv) Personal assistants, automatic speech recognizers and dialogue understanding systems are becoming more critical in our interconnected digital world. A clear example is air traffic control (ATC) communications. ATC aims at guiding aircraft and controlling the airspace in a safe and optimal manner. These voice-based dialogues are carried between an air traffic controller (ATCO) and pilots via very-high frequency radio channels. In order to incorporate these novel technologies into ATC (low-resource domain), large-scale annotated datasets are required to develop the data-driven AI systems. Two examples are automatic speech recognition (ASR) and natural language understanding (NLU). In this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data. The ATCO2 corpus covers 1) data collection and pre-processing, 2) pseudo-annotations of speech data, and 3) extraction of ATC-related named entities. The ATCO2 corpus is split into three subsets. 1) ATCO2-test-set corpus contains 4 hours of ATC speech with manual transcripts and a subset with gold annotations for named-entity recognition (callsign, command, value). 2) The ATCO2-PL-set corpus consists of 5281 hours of unlabeled ATC data enriched with automatic transcripts from an in-domain speech recognizer, contextual information, speaker turn information, signal-to-noise ratio estimate and English language detection score per sample. Both available for purchase through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. 3) The ATCO2-test-set-1h corpus is a one-hour subset from the original test set corpus, that we are offering for free at https://www.atco2.org/data. We expect the ATCO2 corpus will foster research on robust ASR and NLU not only in the field of ATC communications but also in the general research community.