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.

Popis česky
Projekt se věnuje automatickému sběru a zpracování hlasových dat z letecké komunikace. Výsledná data budou sloužit pto trénování systémů rozpoznávání řeči. Cílem projektu je vybudovat kolaborativní platformu pro sběr, automatické a manuální zpracování dat tak, aby byla k užitku jak nadšencům pro letectví, tak velkým i malým firmám zabývajících se avionikou, řízením a bezpečností letového provozu.

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

Klíčová slova česky
řízení letového provozu, automatické rozpoznávání řeči, zpracování signálů, právní a etický rámec

angličtina

Černocký Jan, prof. Dr. Ing. - hlavní řešitel
Kocour Martin, Ing. - spoluřešitel
Pulugundla Bhargav, M.Sc. - spoluřešitel
Žižka Josef, Ing. - spoluřešitel

Ústav počítačové grafiky a multimédií
- odpovědné pracoviště (1.2.2019 - nezadáno)
Výzkumná skupina dolování dat z řeči BUT Speech@FIT
- interní (1.2.2019 - 28.2.2022)
Ústav počítačové grafiky a multimédií
- příjemce (1.2.2019 - 28.2.2022)

ZULUAGA-GOMEZ, J.; VESELÝ, K.; SZŐKE, I.; BLATT, A.; MOTLÍČEK, P.; KOCOUR, M.; RIGAULT, M.; CHOUKRI, K.; PRASAD, A.; SARFJOO, S.; NIGMATULINA, I.; CEVENINI, C.; KOLČÁREK, P.; TART, A.; ČERNOCKÝ, J.; KLAKOW, D. ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications has been verified and confirmed by the Action Editor. Journal of Machine Learning Research, vol. 2, no. 1, p. 1-45. ISSN: 1533-7928.
Detail

SZŐKE, I.; KESIRAJU, S.; NOVOTNÝ, O.; KOCOUR, M.; VESELÝ, K.; ČERNOCKÝ, J. Detecting English Speech in the Air Traffic Control Voice Communication. Proceedings of Interspeech 2021. Brno: 2021. p. 246-250.
Detail

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

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

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

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

Odpovědnost: Černocký Jan, prof. Dr. Ing.