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Title:

Automatic Speech Recognition for Air Traffic Control Communications

Accession Number:

01763712

Record Type:

Component

Availability:

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Order URL: http://worldcat.org/issn/03611981

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.

Supplemental Notes:

Sandeep Badrinath https://orcid.org/0000-0002-9094-1079 © National Academy of Sciences: Transportation Research Board 2021.

Report/Paper Numbers:

TRBAM-21-00234

Language:

English

Authors:

Badrinath, Sandeep

ORCID 0000-0002-9094-1079

Balakrishnan, Hamsa

ORCID 0000-0002-8624-7041

Pagination:

pp 798-810

Publication Date:

2022-1

Serial:

Transportation Research Record: Journal of the Transportation Research Board

Volume: 2676
Issue Number: 1
Publisher: Sage Publications, Incorporated
ISSN: 0361-1981
EISSN: 2169-4052
Serial URL: http://journals.sagepub.com/home/trr

Media Type:

Digital/other

Features:

Figures; References (41) ; Tables

Subject Areas:

Aviation; Data and Information Technology; Operations and Traffic Management

Files:

TRIS, TRB, ATRI

Created Date:

Dec 23 2020 11:09AM