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Building Blocks of Assistant Based Speech Recognition for Air Traffic Management Applications

Kleinert, Matthias and Helmke, Hartmut and Ehr, Heiko and Kern, Christian and Klakow, Dietrich and Motlicek, Petr and Singh, Mittul and Siol, Gerald (2018) Building Blocks of Assistant Based Speech Recognition for Air Traffic Management Applications. In: SESAR Innovation Days. SESAR Innovation Days 2018, 03. - 07. Dez. 2018, Salzburg, Österreich.

Full text not available from this repository.

Abstract

In air traffic control rooms around the world paper flight strips are replaced through different digital solutions. This enables other systems to access the instructed air traffic controller (ATCo) commands and use them for other purposes. Digital flight strip solutions, however, require manual input from the ATCo and, therefore, increase the workload. Recently the AcListant® project has validated that Assistant Based Speech Recognition (ABSR, which integrates a speech recognizer with an assistant system) could be a solution to avoid this increase of workload. However, adaptation of ABSR to new environments usually requires a lot of data, time and expertise, which makes the process expensive. The MALORCA project used machine learning (ML) algorithms to provide a generic, cheap and effective approach for adaptation. Therefore, ABSR was divided into conceptual modules that contain generic parts (building blocks) and domain specific models. As first show case ABSR was auto-matically adapted with radar data and voice recordings from Prague and Vienna approach. The fully trained system reaches command recognition rates (RR) of 92% (Prague) resp. 83% (Vienna) and command recognition error rates (ER) of 0.6% (Prague) resp. 3.2% (Vienna). The building blocks and models and their effect on RR and ER are presented in this paper.

Item URL in elib:https://elib.dlr.de/123233/
Document Type:Conference or Workshop Item (Speech)
Title:Building Blocks of Assistant Based Speech Recognition for Air Traffic Management Applications
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Kleinert, MatthiasMatthias.Kleinert (at) dlr.deUNSPECIFIED
Helmke, HartmutHartmut.Helmke (at) dlr.deUNSPECIFIED
Ehr, HeikoHeiko.Ehr (at) dlr.deUNSPECIFIED
Kern, ChristianChristian.Kern (at) austrocontrol.atUNSPECIFIED
Klakow, Dietrichdietrich.klakow (at) lsv.uni-saarland.deUNSPECIFIED
Motlicek, PetrPetr.Motlicek (at) idiap.chUNSPECIFIED
Singh, Mittulmittul.singh (at) lsv.uni-saarland.deUNSPECIFIED
Siol, GeraldGerald.Siol (at) dlr.deUNSPECIFIED
Date:December 2018
Journal or Publication Title:SESAR Innovation Days
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Status:Published
Keywords:Machine Learning, Assistant Based Speech Recognition, Building Blocks, Automatic Speech Recognition
Event Title:SESAR Innovation Days 2018
Event Location:Salzburg, Österreich
Event Type:international Conference
Event Dates:03. - 07. Dez. 2018
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
Location: Braunschweig
Institutes and Institutions:Institute of Flight Control > Controller Assistance
Deposited By: Kleinert, Matthias
Deposited On:26 Nov 2018 14:36
Last Modified:27 Dec 2018 13:39

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