# Automatic Speech Recognition Benchmark for Air-Traffic Communications

arXiv: https://arxiv.org/abs/2006.10304 · HTML: https://mlsanity.com/paper/2006.10304/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 17 of 17)

- [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)
- [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)
- [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)
- [A two-step approach to leverage contextual data: speech recognition in air-traffic communications](https://mlsanity.com/paper/2202.03725/) (2022)
- [A Comparative Study of Speaker Role Identification in Air Traffic Communication Using Deep Learning Approaches](https://mlsanity.com/paper/2111.02041/) (2021)
- [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)
- [Grammar Based Speaker Role Identification for Air Traffic Control Speech Recognition](https://mlsanity.com/paper/2108.12175/) (2021)
- [Speech recognition for air traffic control via feature learning and end-to-end training](https://mlsanity.com/paper/2111.02654/) (2021)
- [Call-sign recognition and understanding for noisy air-traffic transcripts using surveillance information](https://mlsanity.com/paper/2204.06309/) (2022)
- [Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator](https://mlsanity.com/paper/2212.07164/) (2022)
- [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)
- [RECOVER: Robust Entity Correction via agentic Orchestration of hypothesis Variants for Evidence-based Recovery](https://mlsanity.com/paper/2603.16411/) (2026)
- [Safety-Oriented Evaluation of Language Understanding Systems for Air Traffic Control](https://mlsanity.com/paper/2605.11769/) (2026)
- [Beyond Semantic Accuracy: Consequence-Aware Evaluation for Safety-Critical Language Understanding](https://mlsanity.com/paper/2608.24621/) (2026)

## Abstract (arXiv)

Advances in Automatic Speech Recognition (ASR) over the last decade opened new areas of speech-based automation such as in Air-Traffic Control (ATC) environment. Currently, voice communication and data links communications are the only way of contact between pilots and Air-Traffic Controllers (ATCo), where the former is the most widely used and the latter is a non-spoken method mandatory for oceanic messages and limited for some domestic issues. ASR systems on ATCo environments inherit increasing complexity due to accents from non-English speakers, cockpit noise, speaker-dependent biases, and small in-domain ATC databases for training. Hereby, we introduce CleanSky EC-H2020 ATCO2, a project that aims to develop an ASR-based platform to collect, organize and automatically pre-process ATCo speech-data from air space. This paper conveys an exploratory benchmark of several state-of-the-art ASR models trained on more than 170 hours of ATCo speech-data. We demonstrate that the cross-accent flaws due to speakers' accents are minimized due to the amount of data, making the system feasible for ATC environments. The developed ASR system achieves an averaged word error rate (WER) of 7.75% across four databases. An additional 35% relative improvement in WER is achieved on one test set when training a TDNNF system with byte-pair encoding.