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| Seuring, Stefan |
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| Nor Azizi, S. |
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| Pato, Margarida Vaz |
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| Kölker, Katrin |
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| Huber, Oliver |
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| Király, Tamás |
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| Spengler, Thomas Stefan |
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| Al-Ammar, Essam A. |
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| Dargahi, Fatemeh |
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| Mota, Rui |
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| Mazalan, Nurul Aliah Amirah |
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| Macharis, Cathy | Brussels |
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| Arunasari, Yova Tri |
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| Nunez, Alfredo | Delft |
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| Bouhorma, Mohammed |
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| Bonato, Matteo |
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| Fitriani, Ira |
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| Autor Correspondente Coelho, Sílvia. |
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| Pond, Stephen |
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| Okwara, Ukoha Kalu |
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| Toufigh, Vahid |
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| Campisi, Tiziana | Enna |
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| Ermolieva, Tatiana |
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| Sánchez-Cambronero, Santos |
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| Agzamov, Akhror |
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Rataj, Jürgen
in Cooperation with on an Cooperation-Score of 37%
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Publications (10/10 displayed)
- 2021AcListant with Continuous Learning: Speech Recognition in Air Traffic Control (EIWAC 2019)
- 2021Macroscopic Analysis to identify Stage Boundaries in Multi-Stage Arrival Management
- 2020Self-Structuring Route Network for Free Route Trafficcitations
- 2019Macroscopic Analysis to Identify Stage Boundaries in Multi-Stage Arrival Management
- 2019AcListant with Continuous Learning: Speech Recognition in Air Traffic Controlcitations
- 2014Konzeptuelle Betrachtung nutzer- und situationsbasierter adaptiver Automatisierung in Flug- und Fahrzeugführung
- 2010Iterative design process for the development and testing of cooperative applications
- 2009Um die Ecke geschaut – wenn das Auto mehr sieht als der Fahrer
- 2009AIM – APPLICATION PLATFORM INTELLIGENT MOBILITY
- 2006What is difficult at intersections? Virtual and real driving
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document
AcListant with Continuous Learning: Speech Recognition in Air Traffic Control (EIWAC 2019)
Abstract
Increasing air traffic creates many challenges for air traffic management (ATM). A general answer to these challenges is to increase automation. However, communication between air traffic controllers (ATCos) and pilots is widely analog and far away from digital ATM components. As communication content is important for the ATM system, commands are still entered manually by ATCos to enable the ATM system to consider the communication. However, the disadvantage is an additional workload for the ATCos. To avoid this additional effort, automatic speech recognition (ASR) can automatically analyze the communication and extract the content of spoken commands. DLR together with Saarland University invented the AcListant® system, the first assistant based speech recognition (ABSR) with both a high command recognition rate and a low command recognition error rate. Beside the high recognition performance, AcListant® project revealed shortcomings with respect to costly adaptations of the speech recognizer to different environments. Machine learning algorithms for the automatic adaptation of ABSR to different airports were developed to counteract this disadvantage within the Single European Sky ATM Research Programme (SESAR) 2020 Exploratory Research project MALORCA. To support the standardization of speech recognition in ATM, an ontology for ATC command recognition on semantic level was developed to enable the reuse of expensively manually transcribed ATC communication in the SESAR Industrial Research project PJ.16-04. Finally, results and experiences are used in two further SESAR Wave-2 projects. This paper presents the evolution of ABSR from AcListant® via MALORCA, PJ.16-04 to SESAR Wave-2 projects.
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