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Reducing Controller Workload by Automatic Speech Recognition Assisted Radar Label Maintenance

Kleinert, Matthias and Helmke, Hartmut and Moos, Sylvain and Hlousek, Petr and Windisch, Christian and Ohneiser, Oliver and Ehr, Heiko and Labreuil, Aline (2019) Reducing Controller Workload by Automatic Speech Recognition Assisted Radar Label Maintenance. SESAR Innovation Days 2019, 2019-12-02 - 2019-12-05, Athen, Griechenland.

Full text not available from this repository.

Abstract

Various new hard- and software centered methods were recently implemented to replace paper flight strips through modern technical solutions. These solutions provide valuable information for other ATM applications about the verbally given guidance instructions from air traffic controllers (ATCos), but also tend to increase ATCo workload. Speech recognition applications, which automatically recognize and input the verbal ATCo instructions into a technical flight strip solution, can compensate the workload increase while maintaining the benefit for other ATM applications. Experiments from the AcListant® project in 2015 have shown that Assistant Based Speech Recognition (ABSR), which combines a conventional speech recognizer with an assis-tant system, can provide adequate recognition quality for the use in air traffic control (ATC) applications. AcListant® used a prototypic radar display implementation and a research proto-type of a speech recognizer. This paper describes the exercise 220 of the SESAR 2020 funded solution PJ.16-04. It used a Commer-cial-Off-The-Shelf (COTS) speech recognition engine instead of a research prototype. Furthermore, a radar display developed by Thales Air Sys served for visualization. Command recognition rates varied greatly between 31% and 82% for different control-lers. However, the concept from ABSR to predict possible ATCo instructions could be integrated with the COTS engine, which significantly decreased the command recognition error rate and led to a variation between only 4.8% and 6.6%, i.e. only a small amount of false recognitions were shown to the ATCo.

Item URL in elib:https://elib.dlr.de/131000/
Document Type:Conference or Workshop Item (Speech)
Title:Reducing Controller Workload by Automatic Speech Recognition Assisted Radar Label Maintenance
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Kleinert, MatthiasUNSPECIFIEDhttps://orcid.org/0000-0002-0782-4147UNSPECIFIED
Helmke, HartmutUNSPECIFIEDhttps://orcid.org/0000-0002-1939-0200UNSPECIFIED
Moos, SylvainUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Hlousek, PetrUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Windisch, ChristianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Ohneiser, OliverUNSPECIFIEDhttps://orcid.org/0000-0002-5411-691XUNSPECIFIED
Ehr, HeikoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Labreuil, AlineUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:December 2019
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:PJ.16-04, Assistant Based Speech Recognition, Automatic Speech Recognition, Checker, Air Traffic Control
Event Title:SESAR Innovation Days 2019
Event Location:Athen, Griechenland
Event Type:international Conference
Event Start Date:2 December 2019
Event End Date:5 December 2019
Organizer:Sesar Joint Undertaking SJU
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:21 Nov 2019 10:23
Last Modified:24 Apr 2024 20:34

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