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AcListant with Continuous Learning: Speech Recognition in Air Traffic Control

Rataj, Jürgen and Helmke, Hartmut and Ohneiser, Oliver (2019) AcListant with Continuous Learning: Speech Recognition in Air Traffic Control. In: 6th ENRI International Workshop on Air Traffic Management and communication, navigation and surveillance, ATM/CNS EIWAC2019. 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. ISBN 978-981334668-0. ISSN 1876-1100.

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Abstract

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.

Item URL in elib:https://elib.dlr.de/130586/
Document Type:Conference or Workshop Item (Speech)
Title:AcListant with Continuous Learning: Speech Recognition in Air Traffic Control
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Rataj, JürgenUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Helmke, HartmutUNSPECIFIEDhttps://orcid.org/0000-0002-1939-0200UNSPECIFIED
Ohneiser, OliverUNSPECIFIEDhttps://orcid.org/0000-0002-5411-691X178238998
Date:October 2019
Journal or Publication Title:6th ENRI International Workshop on Air Traffic Management and communication, navigation and surveillance, ATM/CNS EIWAC2019
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI:10.1007/978-981-33-4669-7_6
ISSN:1876-1100
ISBN:978-981334668-0
Status:Published
Keywords:Automatic Speech Recognition, Assistant Based Speech Recognition, Machine Learning, AcListant®, MALORCA, PJ.16-04, Ontology
Event Title:EIWAC 2019 6th ENRI International Workshop an ATM/CNS
Event Location:Tokyo, Japan
Event Type:international Conference
Event Start Date:29 October 2019
Event End Date:31 October 2019
Organizer:ENRI Electronic Navigation Research Institute
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:air traffic management and operations
DLR - Research area:Aeronautics
DLR - Program:L AO - Air Traffic Management and Operation
DLR - Research theme (Project):L - Efficient Flight Guidance (old)
Location: Braunschweig
Institutes and Institutions:Institute of Flight Guidance > Controller Assistance
Deposited By: Diederich, Kerstin
Deposited On:15 Nov 2019 08:57
Last Modified:18 Feb 2025 10:27

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