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

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

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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.

Item URL in elib:https://elib.dlr.de/137227/
Document Type:Book Section
Title:AcListant with Continuous Learning: Speech Recognition in Air Traffic Control (EIWAC 2019)
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/0000-0002-5411-691XUNSPECIFIED
Date:23 March 2021
Journal or Publication Title:Springer Journal
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI:10.1007/978-981-33-4669-7_6
Page Range:pp. 93-112
Publisher:Springer Verlag
ISSN:1876-1119
ISBN:1876-1100
Status:Published
Keywords:Assistant Based Speech Recognition, Machine Learning, AcListant®, MALORCA, PJ.16-04, Ontology
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:28 Jul 2021 12:24
Last Modified:04 Dec 2023 12:36

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