TY  - JOUR
PY  - 2022//
TI  - Automatic speech recognition for air traffic control communications
JO  - Transportation research record
A1  - Badrinath, Sandeep
A1  - Balakrishnan, Hamsa
SP  - 798
EP  - 810
VL  - 2676
IS  - 1
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0361-1981
UR  - http://dx.doi.org/10.1177/03611981211036359
ID  - ref1
ER  -