# Improving callsign recognition with air-surveillance data in air-traffic communication

arXiv: https://arxiv.org/abs/2108.12156 · HTML: https://mlsanity.com/paper/2108.12156/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Automatic Speech Recognition Benchmark for Air-Traffic Communications](https://mlsanity.com/paper/2006.10304/) (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)
- [A Comparison of Methods for OOV-word Recognition on a New Public Dataset](https://mlsanity.com/paper/2107.08091/) (2021)

## Built on by (most cited first, 11 of 11)

- [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)
- [Zero-shot Domain-sensitive Speech Recognition with Prompt-conditioning Fine-tuning](https://mlsanity.com/paper/2307.10274/) (2023)
- [A two-step approach to leverage contextual data: speech recognition in air-traffic communications](https://mlsanity.com/paper/2202.03725/) (2022)
- [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)
- [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)
- [Utilizing Multimodal Data for Edge Case Robust Call-sign Recognition and Understanding](https://mlsanity.com/paper/2412.20467/) (2024)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) can be used as the assistance of speech communication between pilots and air-traffic controllers. Its application can significantly reduce the complexity of the task and increase the reliability of transmitted information. Evidently, high accuracy predictions are needed to minimize the risk of errors. Especially, high accuracy is required in recognition of key information, such as commands and callsigns, used to navigate pilots. Our results prove that the surveillance data containing callsigns can help to considerably improve the recognition of a callsign in an utterance when the weights of probable callsign n-grams are reduced per utterance. In this paper, we investigate two approaches: (1) G-boosting, when callsigns weights are adjusted at language model level (G) and followed by the dynamic decoder with an on-the-fly composition, and (2) lattice rescoring when callsign information is introduced on top of lattices generated using a conventional decoder. Boosting callsign n-grams with the combination of two methods allowed us to gain 28.4% of absolute improvement in callsign recognition accuracy and up to 74.2% of relative improvement in WER of callsign recognition.