mlsanityData to 14 Oct 2026

ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications

arXiv 2211.04054 ↗First posted 8 Nov 2022cs.CL cs.AI cs.SDcited by 19

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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.

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.

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22 of 80 references.

References · 22

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10 more
  1. 2018The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detectioncited by 13
  2. 2021BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communicationscited by 13
  3. 2020A Survey on Recent Advances in Sequence Labeling from Deep Learning Modelscited by 12
  4. 2021Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systemscited by 12
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  8. 2021Detecting English Speech in the Air Traffic Control Voice Communicationcited by 4
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 1; 2025: 4; 2026: 9.

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  1. 2024TartanAviation: Image, Speech, and ADS-B Trajectory Datasets for Terminal Airspace Operationscited by 7
  2. 2023Implementing contextual biasing in GPU decoder for online ASRcited by 4
  3. 2023A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllerscited by 3
  4. 2025Adapting Automatic Speech Recognition for Accented Air Traffic Control Communicationscited by 3
  5. 2022Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulatorcited by 2
  6. 2025BEST-RQ-Based Self-Supervised Learning for Whisper Domain Adaptationcited by 2
  7. 2023Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understandingcited by 1
  8. 2023On the N-gram Approximation of Pre-trained Language Modelscited by 1
  9. 2025In-domain SSL pre-training and streaming ASRcited by 1
  10. 2026Air Traffic Control Using Large Language Models: Prompt Engineering, Architecture, and Evaluationcited by 1
  11. 2025A Modular, Data-Free Pipeline for Multi-Label Intention Recognition in Transportation Agentic AI Applicationscited by 0
  12. 2026RECOVER: Robust Entity Correction via agentic Orchestration of hypothesis Variants for Evidence-based Recoverycited by 0
7 more
  1. 2026SCOPE: A Lightweight-training LLM Framework for Air Traffic Control Readback Monitoringcited by 0
  2. 2026MoDiCoL: A Modular Diagnostic Continual Learning Dataset for Robust Speech Recognitioncited by 0
  3. 2026ASTRA: A Scalable Next-Generation ATCO Training Simulator with Autonomous Simpilotscited by 0
  4. 2026Synthetic Audio Generation Framework for Air Traffic Control Speech Recognitioncited by 0
  5. 2026Beyond Semantic Accuracy: Consequence-Aware Evaluation for Safety-Critical Language Understandingcited by 0
  6. 2026Formal, Executable and Explainable Runtime Monitoring of Spoken Air Traffic Control Operational Procedurescited by 0
  7. 2026V2TATC: Joint Voice-Trajectory Embedding and Dataset for Air Traffic Controller Situational Awarenesscited by 0