Automatic Flight Callsign Identification on a Controller Working Position: Real-Time Simulation and Analysis of Operational Recordings

Author:

García Raquel1ORCID,Albarrán Juan2,Fabio Adrián1,Celorrio Fernando12,Pinto de Oliveira Carlos3,Bárcena Cristina2

Affiliation:

1. Centro de Referencia I+D+i ATM (CRIDA A.I.E), 28022 Madrid, Spain

2. ENAIRE, 28022 Madrid, Spain

3. EML Speech Technology GmbH, 69120 Heidelberg, Germany

Abstract

In the air traffic management (ATM) environment, air traffic controllers (ATCos) and flight crews, (FCs) communicate via voice to exchange different types of data such as commands, readbacks (confirmation of reception of the command) and information related to the air traffic environment. Speech recognition can be used in these voice exchanges to support ATCos in their work; each time a flight identification or callsign is mentioned by the controller or the pilot, the flight is recognised through automatic speech recognition (ASR) and the callsign is highlighted on the ATCo screen to increase their situational awareness and safety. This paper presents the work that is being performed within SESAR2020-founded solution PJ.10-W2-96 ASR in callsign recognition via voice by Enaire, Indra, and Crida using ASR models developed jointly by EML Speech Technology GmbH (EML) and Crida. The paper describes the ATCo speech environment and presents the main requirements impacting the design, the implementation performed, and the outcomes obtained using real operation communications and real-time simulations. The findings indicate a way forward incorporating partial recognition of callsigns and enriching the phonetization of company names to improve the recognition rates, currently set at 84–87% for controllers and 49–67% for flight crew.

Funder

European Union’s Horizon 2020 research and innovation programme

Publisher

MDPI AG

Subject

Aerospace Engineering

Reference36 articles.

1. ICAO (2016). Procedures for Air Navigation Services (PANS)—Air Traffic Management Doc 4444, ICAO. [16th ed.].

2. ICAO (2018). Annex 11—Air Traffic Services, ICAO. [15th ed.]. Para 3.7.3.1.

3. Helmke, H., Rataj, J., Mühlhausen, T., Ohneiser, O., Ehr, H., Kleinert, M., Oualil, Y., Schulder, M., and Klakow, D. (2015, January 23–26). Assistant-based speech recognition for ATM applications. Proceedings of the 11th USA/Europe Air Traffic Management Research and Development Seminar (ATM 2015), Lisbon, Portugal.

4. Possibilities, challenges and the state of the art of automatic speech recognition in air traffic control;Nguyen;Int. J. Comput. Inf. Eng.,2015

5. The Airbus Air Traffic Control Speech Recognition 2018 Challenge: Towards ATC Automatic Transcription and Call Sign Detection;Pellegrini;Interspeech,2019

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