Applying Large-Scale Weakly Supervised Automatic Speech Recognition to Air Traffic Control

Master thesis (2023)

Authors

Contributors

J. Sun J. Sun (supervisor 1)
Patrick Jonk Patrick Jonk Royal Netherlands Aerospace Centre NLR (supervisor 1)
Vincent de Vries Vincent de Vries Royal Netherlands Aerospace Centre NLR (supervisor 2)
Faculty
Aerospace Engineering
Copyright: © 2023 Jan Laurenszoon van Doorn
ASR Air Traffic Control Automatic Speech Recognition ATC Whisper
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Published Date

11-12-2023

Language

English

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Faculty

Aerospace Engineering

Abstract

The application of automatic speech recognition in the air traffic control domain has been researched extensively. However, its primary application remains in the training and simulation of air traffic controllers. This is due to the insufficient performance of automatic speech recognition in specific environments, such as air traffic control, where strong performance and safety requirements are paramount. This study demonstrates how a large-scale, weakly supervised automatic speech recognition model, Whisper, could meet these performance requirements and establish a new approach to air traffic control communication. Fine-tuning Whisper in the air traffic control domain resulted in a word error rate of 13.5% on the ATCO2 dataset and 1.17% on the ATCOSIM dataset. Furthermore, the study reveals that fine-tuning with region-specific data can enhance performance by up to 60% in real-world scenarios.