Automatic Speech Recognition for Air Traffic Control Communications
Name
BadrinathBalakrishnan-TRR2021.pdf
Description
Accepted version
Size
991.02 KB
Format
Adobe PDF
Checksum (MD5)
c57e4d5656ec61cf85c278978e457de6
Author(s)
Badrinath, Sandeep
Balakrishnan, Hamsa
Date Issued
2022
Journal
Transportation Research Record
Publisher
SAGE Publications
Citation
Badrinath, Sandeep and Balakrishnan, Hamsa. 2022. "Automatic Speech Recognition for Air Traffic Control Communications." Transportation Research Record, 2676 (1).
Version
Author's final manuscript
Abstract
A significant fraction of communications between air traffic controllers and pilots is through speech, via radio channels. Automatic transcription of air traffic control (ATC) communications has the potential to improve system safety, operational performance, and conformance monitoring, and to enhance air traffic controller training. We present an automatic speech recognition model tailored to the ATC domain that can transcribe ATC voice to text. The transcribed text is used to extract operational information such as call-sign and runway number. The models are based on recent improvements in machine learning techniques for speech recognition and natural language processing. We evaluate the performance of the model on diverse datasets.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/03611981211036359
