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Machine Learning of Air Traffic Controller Command Extraction Models for Speech Recognition Applications

Helmke, Hartmut and Kleinert, Matthias and Ohneiser, Oliver and Ehr, Heiko and Shetty, Shruthi (2020) Machine Learning of Air Traffic Controller Command Extraction Models for Speech Recognition Applications. In: 39th AIAA/IEEE Digital Avionics Systems Conference, DASC 2020. 39th Digital Avionics Systems Conference DASC, 2020-10-11 - 2020-10-06, Virtual Conference. doi: 10.1109/DASC50938.2020.9256484. ISBN 978-172819825-5. ISSN 2155-7195.

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Increasing digitization and automation is a widely accepted method to cope with the challenges of constantly increasing air traffic. The analogue communication of air traffic controllers (ATCo) to pilots has been excluded so far from the digitization process. However, the content of this communication is of decisive importance for various automation systems. Although Assistant Based Speech Recognition (ABSR) has recently significantly improved the recognition performance and, therefore, enables the digitization of ATCo-pilot-communication, its adaptation to other airports is a critical and costly process, This is even more important, if ATCos tend to deviate from the published ICAO phraseology: “start reducing to two fifty” instead of “reduce two five zero knots” is just an example. User acceptance requires that these deviations are also correctly recognized. Therefore, this paper presents an approach, which automatically learns a so-called Command Extraction Model from labelled controller utterances. The initial Command Extraction Model without learning only covers 60% of the commands, whereas the automatically learned Command Extraction Model covers more than 98%. With just six hours of training data we could achieve 94%

Item URL in elib:https://elib.dlr.de/137232/
Document Type:Conference or Workshop Item (Speech)
Title:Machine Learning of Air Traffic Controller Command Extraction Models for Speech Recognition Applications
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Helmke, HartmutUNSPECIFIEDhttps://orcid.org/0000-0002-1939-0200UNSPECIFIED
Kleinert, MatthiasUNSPECIFIEDhttps://orcid.org/0000-0002-0782-4147UNSPECIFIED
Ohneiser, OliverUNSPECIFIEDhttps://orcid.org/0000-0002-5411-691XUNSPECIFIED
Journal or Publication Title:39th AIAA/IEEE Digital Avionics Systems Conference, DASC 2020
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Speech Recognition, Machine Learning, Annotation, Ontology, Controller Command Extraction Model
Event Title:39th Digital Avionics Systems Conference DASC
Event Location:Virtual Conference
Event Type:international Conference
Event Start Date:11 October 2020
Event End Date:6 October 2020
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:09 Nov 2020 09:15
Last Modified:24 Apr 2024 20:39

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