# A two-step approach to leverage contextual data: speech recognition in air-traffic communications

arXiv: https://arxiv.org/abs/2202.03725 · HTML: https://mlsanity.com/paper/2202.03725/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 4 of 4)

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

## Built on by (most cited first, 8 of 8)

- [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)
- [Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward](https://mlsanity.com/paper/2411.03866/) (2024)
- [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)
- [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)
- [Race and Privacy in Broadcast Police Communications](https://mlsanity.com/paper/2407.01817/) (2024)

## Abstract (arXiv)

Automatic Speech Recognition (ASR), as the assistance of speech communication between pilots and air-traffic controllers, can significantly reduce the complexity of the task and increase the reliability of transmitted information. ASR application can lead to a lower number of incidents caused by misunderstanding and improve air traffic management (ATM) efficiency. Evidently, high accuracy predictions, especially, of key information, i.e., callsigns and commands, are required to minimize the risk of errors. We prove that combining the benefits of ASR and Natural Language Processing (NLP) methods to make use of surveillance data (i.e. additional modality) helps to considerably improve the recognition of callsigns (named entity). In this paper, we investigate a two-step callsign boosting approach: (1) at the 1 step (ASR), weights of probable callsign n-grams are reduced in G.fst and/or in the decoding FST (lattices), (2) at the 2 step (NLP), callsigns extracted from the improved recognition outputs with Named Entity Recognition (NER) are correlated with the surveillance data to select the most suitable one. Boosting callsign n-grams with the combination of ASR and NLP methods eventually leads up to 53.7% of an absolute, or 60.4% of a relative, improvement in callsign recognition.