# The Airbus Air Traffic Control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detection

arXiv: https://arxiv.org/abs/1810.12614 · HTML: https://mlsanity.com/paper/1810.12614/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://mlsanity.com/paper/2211.04054/) (2022)
- [BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications](https://mlsanity.com/paper/2110.05781/) (2021)
- [Improving speech recognition models with small samples for air traffic control systems](https://mlsanity.com/paper/2102.08015/) (2021)
- [ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems](https://mlsanity.com/paper/2102.08535/) (2021)
- [Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications](https://mlsanity.com/paper/2502.20311/) (2025)
- [Grammar Based Speaker Role Identification for Air Traffic Control Speech Recognition](https://mlsanity.com/paper/2108.12175/) (2021)
- [Call-sign recognition and understanding for noisy air-traffic transcripts using surveillance information](https://mlsanity.com/paper/2204.06309/) (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)
- [Race and Privacy in Broadcast Police Communications](https://mlsanity.com/paper/2407.01817/) (2024)
- [In-domain SSL pre-training and streaming ASR](https://mlsanity.com/paper/2509.12101/) (2025)
- [Utilizing Multimodal Data for Edge Case Robust Call-sign Recognition and Understanding](https://mlsanity.com/paper/2412.20467/) (2024)
- [Synthetic Audio Generation Framework for Air Traffic Control Speech Recognition](https://mlsanity.com/paper/2606.21340/) (2026)

## Abstract (arXiv)

In this paper, we describe the outcomes of the challenge organized and run by Airbus and partners in 2018. The challenge consisted of two tasks applied to Air Traffic Control (ATC) speech in English: 1) automatic speech-to-text transcription, 2) call sign detection (CSD). The registered participants were provided with 40 hours of speech along with manual transcriptions. Twenty-two teams submitted predictions on a five hour evaluation set. ATC speech processing is challenging for several reasons: high speech rate, foreign-accented speech with a great diversity of accents, noisy communication channels. The best ranked team achieved a 7.62% Word Error Rate and a 82.41% CSD F1-score. Transcribing pilots' speech was found to be twice as harder as controllers' speech. Remaining issues towards solving ATC ASR are also discussed.