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AcListant with Continuous Learning: Speech Recognition in Air Traffic Control (EIWAC 2019)

Rataj, Jürgen und Helmke, Hartmut und Ohneiser, Oliver (2021) AcListant with Continuous Learning: Speech Recognition in Air Traffic Control (EIWAC 2019). In: Springer Journal Springer Verlag. Seiten 93-112. doi: 10.1007/978-981-33-4669-7_6. ISBN 1876-1100. ISSN 1876-1119.

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Kurzfassung

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.

elib-URL des Eintrags:https://elib.dlr.de/137227/
Dokumentart:Beitrag in einem Lehr- oder Fachbuch
Titel:AcListant with Continuous Learning: Speech Recognition in Air Traffic Control (EIWAC 2019)
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Rataj, JürgenJuergen.Rataj (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Helmke, Hartmuthartmut.helmke (at) dlr.dehttps://orcid.org/0000-0002-1939-0200NICHT SPEZIFIZIERT
Ohneiser, OliverOliver.Ohneiser (at) dlr.dehttps://orcid.org/0000-0002-5411-691XNICHT SPEZIFIZIERT
Datum:23 März 2021
Erschienen in:Springer Journal
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
DOI:10.1007/978-981-33-4669-7_6
Seitenbereich:Seiten 93-112
Verlag:Springer Verlag
ISSN:1876-1119
ISBN:1876-1100
Status:veröffentlicht
Stichwörter:Assistant Based Speech Recognition, Machine Learning, AcListant®, MALORCA, PJ.16-04, Ontology
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Luftfahrt
HGF - Programmthema:Luftverkehrsmanagement und Flugbetrieb
DLR - Schwerpunkt:Luftfahrt
DLR - Forschungsgebiet:L AO - Air Traffic Management and Operation
DLR - Teilgebiet (Projekt, Vorhaben):L - Effiziente Flugführung (alt)
Standort: Braunschweig
Institute & Einrichtungen:Institut für Flugführung > Lotsenassistenz
Hinterlegt von: Diederich, Kerstin
Hinterlegt am:28 Jul 2021 12:24
Letzte Änderung:04 Dez 2023 12:36

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