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Proceeding Paper

Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications †

1
Idiap Research Institute, 1920 Martigny, Switzerland
2
Signal Processing Lab (LTS5), 1 Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
3
[email protected] and IT4I Center of Excellence, Brno University of Technology, 60190 Brno, Czechia
4
Department of Language Science and Technology, Saarland University, 66123 Saarbruecken, Germany
5
OpenSky Network, 3400 Burgdorf, Switzerland
6
ReplayWell, 61600 Brno, Czech Republic
7
Honeywell, 62700 Brno, Czech Republic
8
Romagna Tech, 47121 Forli, Italy
9
Evaluations and Language Resources Distribution Agency (ELDA), 75013 Paris, France
*
Author to whom correspondence should be addressed.
Presented at the 8th OpenSky Symposium 2020, Online, 12–13 November 2020.
Proceedings 2020, 59(1), 14; https://doi.org/10.3390/proceedings2020059014
Published: 3 December 2020
(This article belongs to the Proceedings of 8th OpenSky Symposium 2020)
Voice communication is the main channel to exchange information between pilots and Air-Traffic Controllers (ATCos). Recently, several projects have explored the employment of speech recognition technology to automatically extract spoken key information such as call signs, commands, and values, which can be used to reduce ATCos’ workload and increase performance and safety in Air-Traffic Control (ATC)-related activities. Nevertheless, the collection of ATC speech data is very demanding, expensive, and limited to the intrinsic speakers’ characteristics. As a solution, this paper presents ATCO2, a project that aims to develop a unique platform to collect, organize, and pre-process ATC data collected from air space. Initially, the data are gathered directly through publicly accessible radio frequency channels with VHF receivers and LiveATC, which can be considered as an “unlimited-source” of low-quality data. The ATCO2 project explores employing context information such as radar and air surveillance data (collected with ADS-B and Mode S) from the OpenSky Network (OSN) to correlate call signs automatically extracted from voice communication with those available from ADS-B channels, to eventually increase the overall call sign detection rates. More specifically, the timestamp and location of the spoken command (issued by the ATCo by voice) are extracted, and a query is sent to the OSN server to retrieve the call sign tags in ICAO format for the airplanes corresponding to the given area. Then, a word sequence provided by an automatic speech recognition system is fed into a Natural Language Processing (NLP) based module together with the set of call signs available from the ADS-B channels. The NLP module extracts the call sign, command, and command arguments from the spoken utterance.
Keywords: air traffic control; air surveillance data; automatic speech recognition; call sign detection; OpenSky Network; named entity recognition air traffic control; air surveillance data; automatic speech recognition; call sign detection; OpenSky Network; named entity recognition
MDPI and ACS Style

Zuluaga-Gomez, J.; Veselý, K.; Blatt, A.; Motlicek, P.; Klakow, D.; Tart, A.; Szöke, I.; Prasad, A.; Sarfjoo, S.; Kolčárek, P.; Kocour, M.; Černocký, H.; Cevenini, C.; Choukri, K.; Rigault, M.; Landis, F. Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications. Proceedings 2020, 59, 14. https://doi.org/10.3390/proceedings2020059014

AMA Style

Zuluaga-Gomez J, Veselý K, Blatt A, Motlicek P, Klakow D, Tart A, Szöke I, Prasad A, Sarfjoo S, Kolčárek P, Kocour M, Černocký H, Cevenini C, Choukri K, Rigault M, Landis F. Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications. Proceedings. 2020; 59(1):14. https://doi.org/10.3390/proceedings2020059014

Chicago/Turabian Style

Zuluaga-Gomez, Juan, Karel Veselý, Alexander Blatt, Petr Motlicek, Dietrich Klakow, Allan Tart, Igor Szöke, Amrutha Prasad, Saeed Sarfjoo, Pavel Kolčárek, Martin Kocour, Honza Černocký, Claudia Cevenini, Khalid Choukri, Mickael Rigault, and Fabian Landis. 2020. "Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications" Proceedings 59, no. 1: 14. https://doi.org/10.3390/proceedings2020059014

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