Project Details

Automatic collection and processing of voice data from air-traffic communications

Project Period: 1. 11. 2019 – 28. 2. 2022

Project Type: grant

Agency: Evropská unie

Program: Horizon 2020

Czech title
Automatický sběr a zpracování hlasových dat z letecké komunikace
Type
grant
Keywords

air-traffic management, automatic speech recognition, signal processing, legal and ethical framework

Abstract

Developing machine learning solutions for air-traffic control applications is a challenging task. Besides an expert knowledge, large amount of data for robust performance as well as for validation and verification is typically required. If funded, ATCO2 will deliver a unique platform enabling to collect, store, pre-process and share voice communications data recorded from real world air-traffic control data. The project aims at accessing data from two sources: (a) from certified ADS-B datalinks aligned with a surveillance technology, and (b) directly from air-traffic controllers offered to the project by several air navigation service providers. The technical development will be centred around the ATCO2 platform, built on an existing and extensively used solution of opensky-network partner, ensuring sustainability of the platform after the end of the project. Current platform collects periodically broadcasted aircraft information through a network of ADS-B receivers operated around the globe, further stored at a server. In ATCO2, existing platform will be extended to allow collection, storage and pre-processing of voice communications, and time/position aligned with other aircraft information. Unlike previous works, we will target both channels, i.e. spoken commands issued by air-traffic controllers, and confirmation provided by pilots. In addition to broadcasted data, ATCO2 will have an access to voice recordings from air navigation service providers, namely Austrocontrol. This data will simulate other source of speech recordings (specifically archives), complementing real-time voice communication. The ATCO2 platform will be enhanced by the latest speech pre-processing and machine learning technologies, mostly based on deep learning. Besides automatic segmentation (e.g. er speaker, accent, specific command), robust automatic speech recognition system will be implemented and integrated through RESTful API allowing to automatically transcribe voice communications.

Team members
Černocký Jan, prof. Dr. Ing. (DCGM) – research leader
Kocour Martin, Ing. (DCGM)
Pulugundla Bhargav, M.Sc.
Veselý Karel, Ing., Ph.D. (DCGM)
Žižka Josef, Ing. (DCGM)
Publications

2023

  • ZULUAGA-GOMEZ, J.; NIGMATULINA, I.; PRASAD, A.; MOTLÍČEK, P.; KHALIL, D.; MADIKERI, S.; TART, A.; SZŐKE, I.; LENDERS, V.; RIGAULT, M.; CHOUKRI, K. Lessons Learned in Transcribing 5000 h of Air Traffic Control Communications for Robust Automatic Speech Understanding. Aerospace, 2023, vol. 2023, no. 10, p. 1-33. ISSN: 2226-4310. Detail
  • ZULUAGA-GOMEZ, J.; PRASAD, A.; NIGMATULINA, I.; SARFJOO, S.; MOTLÍČEK, P.; KLEINERT, M.; HELMKE, H.; OHNEISER, O.; ZHAN, Q. How Does Pre-Trained Wav2Vec 2.0 Perform on Domain-Shifted ASR? an Extensive Benchmark on Air Traffic Control Communications. In IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings. Doha: IEEE Signal Processing Society, 2023. p. 205-212. ISBN: 978-1-6654-7189-3. Detail

2022

  • BLATT, A.; KOCOUR, M.; VESELÝ, K.; SZŐKE, I.; KLAKOW, D. Call-Sign Recognition and Understanding for Noisy Air-Traffic Transcripts Using Surveillance Information. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Singapore: IEEE Signal Processing Society, 2022. p. 8357-8361. ISBN: 978-1-6654-0540-9. Detail
  • KOCOUR, M.; ŽMOLÍKOVÁ, K.; ONDEL YANG, L.; ŠVEC, J.; DELCROIX, M.; OCHIAI, T.; BURGET, L.; ČERNOCKÝ, J. Revisiting joint decoding based multi-talker speech recognition with DNN acoustic model. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. Proceedings of Interspeech. Incheon: International Speech Communication Association, 2022. p. 4955-4959. ISSN: 1990-9772. Detail
  • NIGMATULINA, I.; ZULUAGA-GOMEZ, J.; PRASAD, A.; SARFJOO, S.; MOTLÍČEK, P. A Two-Step Approach to Leverage Contextual Data: Speech Recognition in Air-Traffic Communications. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Singapore: IEEE Signal Processing Society, 2022. p. 6282-6286. ISBN: 978-1-6654-0540-9. Detail
  • PRASAD, A.; ZULUAGA-GOMEZ, J.; MOTLÍČEK, P.; SARFJOO, S.; NIGMATULINA, I.; OHNEISER, O.; HELMKE, H. Grammar Based Speaker Role Identification for Air Traffic Control Speech Recognition. Proceedings of the 12th SESAR Innovation Days. Budapest: 2022. p. 1-9. Detail
  • PRASAD, A.; ZULUAGA-GOMEZ, J.; MOTLÍČEK, P.; SARFJOO, S.; NIGMATULINA, I.; VESELÝ, K. Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator. Proceedings of the 12th SESAR Innovation Days. Budapest: 2022. p. 1-9. Detail

2021

  • KOCOUR, M.; CÁMBARA, G.; LUQUE, J.; BONET, D.; FARRÚS, M.; KARAFIÁT, M.; VESELÝ, K.; ČERNOCKÝ, J. BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge. Proceedings of IberSPEECH 2021. Vallaloid: International Speech Communication Association, 2021. p. 113-117. Detail
  • KOCOUR, M.; VESELÝ, K.; BLATT, A.; ZULUAGA-GOMEZ, J.; SZŐKE, I.; ČERNOCKÝ, J.; KLAKOW, D.; MOTLÍČEK, P. Boosting of Contextual Information in ASR for Air-Traffic Call-Sign Recognition. In Proceedings Interspeech 2021. Proceedings of Interspeech. Brno: International Speech Communication Association, 2021. p. 3301-3305. ISSN: 1990-9772. Detail
  • KOCOUR, M.; VESELÝ, K.; SZŐKE, I.; KESIRAJU, S.; ZULUAGA-GOMEZ, J.; BLATT, A.; PRASAD, A.; NIGMATULINA, I.; MOTLÍČEK, P.; KLAKOW, D.; TART, A.; KOLČÁREK, P.; ČERNOCKÝ, J.; CEVENINI, C.; CHOUKRI, K.; RIGAULT, M.; LANDIS, F.; SARFJOO, S. Automatic Processing Pipeline for Collecting and Annotating Air-Traffic Voice Communication Data. In Proceedings of 9th OpenSky Symposium 2021, OpenSky Network, Brussels, Belgium. Proceedings. Brussels: MDPI, 2021. p. 1-10. ISSN: 2504-3900. Detail
  • SZŐKE, I.; KESIRAJU, S.; NOVOTNÝ, O.; KOCOUR, M.; VESELÝ, K.; ČERNOCKÝ, J. Detecting English Speech in the Air Traffic Control Voice Communication. In Proceedings Interspeech 2021. Proceedings of Interspeech. Brno: International Speech Communication Association, 2021. p. 3286-3290. ISSN: 1990-9772. Detail
  • VYDANA, H.; KARAFIÁT, M.; ŽMOLÍKOVÁ, K.; BURGET, L.; ČERNOCKÝ, J. Jointly Trained Transformers Models for Spoken Language Translation. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toronto, Ontario: IEEE Signal Processing Society, 2021. p. 7513-7517. ISBN: 978-1-7281-7605-5. Detail
  • ZULUAGA-GOMEZ, J.; NIGMATULINA, I.; PRASAD, A.; MOTLÍČEK, P.; VESELÝ, K.; KOCOUR, M.; SZŐKE, I. Contextual Semi-Supervised Learning: An Approach to Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems. In Proceedings Interspeech 2021. Proceedings of Interspeech. Brno: International Speech Communication Association, 2021. p. 3296-3300. ISSN: 1990-9772. Detail

2020

  • ZULUAGA-GOMEZ, J.; MOTLÍČEK, P.; ZHAN, Q.; VESELÝ, K.; BRAUN, R. Automatic Speech Recognition Benchmark for Air-Traffic Communications. In Proceedings of Interspeech 2020. Proceedings of Interspeech. Shanghai: International Speech Communication Association, 2020. p. 2297-2301. ISSN: 1990-9772. Detail
  • ZULUAGA-GOMEZ, J.; VESELÝ, K.; BLATT, A.; MOTLÍČEK, P.; KLAKOW, D.; TART, A.; SZŐKE, I.; PRASAD, A.; SARFJOO, S.; KOLČÁREK, P.; KOCOUR, M.; ČERNOCKÝ, J.; CEVENINI, C.; CHOUKRI, K.; RIGAULT, M.; LANDIS, F. Automatic Call Sign Detection: Matching Air Surveillance Data with Air Traffic Spoken Communications. Proceedings of the 8th OpenSky Symposium 2020. Proceedings. Brusel: MDPI, 2020. p. 1-10. ISSN: 2504-3900. Detail
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