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AcListant with Continuous Learning: Speech Recognition in Air Traffic Control

Rataj, Jürgen und Helmke, Hartmut und Ohneiser, Oliver (2019) AcListant with Continuous Learning: Speech Recognition in Air Traffic Control. EIWAC 2019 6th ENRI International Workshop an ATM/CNS, 2019-10-29 - 2019-10-31, Tokyo, Japan. doi: 10.1007/978-981-33-4669-7_6.

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Kurzfassung

Increasing air traffic creates many challenges for 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 entered manually by the ATCo, to enable the ATM system to react to the communication. However, the disadvantage is an additional workload of ATCos. To avoid this effort automatic speech recognition (ASR) can automatically analyze the communication and extract the content of commands. To achieve low failure rates, DLR together with Saarland University invented the AcListant® system, the first assistant based speech recognition (ABSR). AcListant® validation trials reveal also shortcomings, like problems with the costly adaptations of the recognizer to specific environments. SESAR 2020 Exploratory Research funded project MALORCA developed machine learning algorithms to automatically adapt ABSR to different airports. SESAR Industrial Research funded solution PJ 16-04 developed an ontology for ATC command transcription to enable reuse of expensive manually transcribed ATC communication. Finally, results and experiences are used in SESAR Wave-2 Solutions 96 and 97. This paper presents the evolution from AcListant® via MALORCA, PJ.16-04 to Wave-2 Solutions 96 and 97.

elib-URL des Eintrags:https://elib.dlr.de/130586/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:AcListant with Continuous Learning: Speech Recognition in Air Traffic Control
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:Oktober 2019
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
Status:veröffentlicht
Stichwörter:Automatic Speech Recognition, Assistant Based Speech Recognition, Machine Learning, AcListant®, MALORCA, PJ.16-04, Ontology
Veranstaltungstitel:EIWAC 2019 6th ENRI International Workshop an ATM/CNS
Veranstaltungsort:Tokyo, Japan
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:29 Oktober 2019
Veranstaltungsende:31 Oktober 2019
Veranstalter :ENRI Electronic Navigation Research Institute
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:15 Nov 2019 08:57
Letzte Änderung:24 Apr 2024 20:34

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