# Call-sign recognition and understanding for noisy air-traffic transcripts using surveillance information

arXiv: https://arxiv.org/abs/2204.06309 · HTML: https://mlsanity.com/paper/2204.06309/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://mlsanity.com/paper/1907.12461/) (2019)
- [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)
- [Improving callsign recognition with air-surveillance data in air-traffic communication](https://mlsanity.com/paper/2108.12156/) (2021)

## Built on by (most cited first, 2 of 2)

- [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)
- [Utilizing Multimodal Data for Edge Case Robust Call-sign Recognition and Understanding](https://mlsanity.com/paper/2412.20467/) (2024)

## Abstract (arXiv)

Air traffic control (ATC) relies on communication via speech between pilot and air-traffic controller (ATCO). The call-sign, as unique identifier for each flight, is used to address a specific pilot by the ATCO. Extracting the call-sign from the communication is a challenge because of the noisy ATC voice channel and the additional noise introduced by the receiver. A low signal-to-noise ratio (SNR) in the speech leads to high word error rate (WER) transcripts. We propose a new call-sign recognition and understanding (CRU) system that addresses this issue. The recognizer is trained to identify call-signs in noisy ATC transcripts and convert them into the standard International Civil Aviation Organization (ICAO) format. By incorporating surveillance information, we can multiply the call-sign accuracy (CSA) up to a factor of four. The introduced data augmentation adds additional performance on high WER transcripts and allows the adaptation of the model to unseen airspaces.