Project Details
HAAWAII - Highly Automated Air Traffic Controller Workstations with Artificial Intelligence Integration
Project Period: 1. 6. 2020 – 30. 11. 2022
Project Type: grant
Code: H2020-SESAR-2019-2
Agency: Evropská unie
Program: Horizon 2020
Artificial Intelligence , Machine Learning, Air-Traffic Control, Natural Language
Processing, Automatic Speech Recognition,
Advanced automation support developed in Wave 1 of SESAR IR includes using of
automatic speech recognition (ASR) to
reduce the amount of manual data inputs by air-traffic controllers. Evaluation of
controllers feedback has been subdued due
to the limited recognition performance of the commercial of the shell ASR engines
that were used, even in laboratory
conditions. The reasons for the unsatisfactory conclusions include e.g. inability
to distinguish controllers accents, deviations
from standard phraseology and limited real-time recognition performance. Past
exploratory research funded project
MALORCA, however, has shown (on restricted use-cases) that satisfactory
performance can be reached with novel datadriven
machine learning approaches.
Based on the results of MALORCA HAAWAII project aims to research and develop
a reliable, error resilient and adaptable
solution to automatically transcribe voice commands issued by both air-traffic
controllers and pilots. The project will build on
very large collection of data, organized with a minimum expert effort to develop
a new set of models for complex
environments of Icelandic en-route and London TMA. HAAWAII aims to perform
proof-of-concept trials in challenging
environments, i.e. to be directly connected with real-life data from ops room. As
pilot read-back error detection is the main
application, HAAWAII aims to significantly enhance the validity of the speech
recognition models. The proposed work goes
far beyond the work planned for the Wave 2 IR programme and will improve both
safety and reduce controllers workload.
The digitization of controller and pilot voice utterances can be used for a wide
variety of safety and performance related
benefits including, but not limiting to pre-fill entries into electronic flight
strips and CPDLC messages. Another application
demonstrated during proof-of-concept will be to objectively estimate controllers
workload utilising digitized voice recordings
of the complex London TMA.
Doležal Jan, Ing. (DCGM)
Dytrych Jaroslav, Ing., Ph.D. (DCGM)
Hradiš Michal, Ing., Ph.D. (DCGM)
Jírovec Martin, Ing. (DFIT-Dean)
Musil Martin, Ing., Ph.D. (DCGM)
Otrusina Lubomír, Ing. (DCGM)
Veselý Karel, Ing., Ph.D. (DCGM)
2023
- MOTLÍČEK, P.; PRASAD, A.; NIGMATULINA, I.; HELMKE, H.; OHNEISER, O.; KLEINERT, M. Automatic Speech Analysis Framework for ATC Communication in HAAWAII. In SESAR Innovation Days. Seville: SESAR Joint Undertaking, 2023.
p. 1-9. ISSN: 0770-1268. Detail - ZULUAGA-GOMEZ, J.; SARFJOO, S.; PRASAD, A.; NIGMATULINA, I.; MOTLÍČEK, P.; ONDŘEJ, K.; OHNEISER, O.; HELMKE, H. BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications. In IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings. Doha: IEEE Signal Processing Society, 2023.
p. 633-640. ISBN: 978-1-6654-7189-3. Detail
2022
- HELMKE, H.; ONDŘEJ, K.; SHETTY, S.; KLEINERT, M.; OHNEISER, O.; EHR, H.; ZULUAGA-GOMEZ, J.; SMRŽ, P. Readback Error Detection by Automatic Speech Recognition and Understanding - Results of HAAWAII project for Isavia's Enroute Airspace. SESAR Innovation Days 2022. Budapest: 2022.
p. 1-9. 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
- HELMKE, H.; KLEINERT, M.; SHETTY, S.; OHNEISER, O.; EHR, H.; PRASAD, A.; MOTLÍČEK, P.; VESELÝ, K.; ONDŘEJ, K.; SMRŽ, P.; HARFMANN, J.; WINDISCH, C. Readback Error Detection by Automatic Speech Recognition to Increase ATM Safety. In Proceedings of ATM Seminar. on-line: EUROPEAN ORGANISATION FOR THE SAFETY OF AIR NAVIGATION, 2021.
p. 1-10. Detail - HELMKE, H.; SHETTY, S.; KLEINERT, M.; OHNEISER, O.; EHR, H.; MOTLÍČEK, P.; PRASAD, A.; WINDISCH, C. Measuring Speech Recognition And Understanding Performance in Air Traffic Control Domain Beyond Word Error Rates. In Proceedings of 11th SESAR Innovation Days 2021. Belgie: 2021.
p. 1-8. Detail - KLEINERT, M.; HELMKE, H.; SHETTY, S.; OHNEISER, O.; EHR, H.; PRASAD, A.; MOTLÍČEK, P.; HARFMANN, J. Automated Interpretation of Air Traffic Control Communication: The Journey from Spoken Words to a Deeper Understanding of the Meaning. In Proceedings of DASC 2021. San Antonio, Texas: Institute of Electrical and Electronics Engineers, 2021.
p. 1-9. ISBN: 978-1-6654-3420-1. 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 - 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.; 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