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 [BibTeX] [Marc21]
Adaptation of Assistant Based Speech Recognition to New Domains and Its Acceptance by Air Traffic Controllers
Type of publication: Conference paper
Citation: Kleinert_IHSI2019_2019
Publication status: Accepted
Booktitle: Proceedings of the 2nd International Conference on Intelligent Human Systems Integration (IHSI 2019): Integrating People and Intelligent Systems
Year: 2019
Month: February
Pages: 820 - 826
Location: San Diego, California, USA
DOI: 10.1007/978-3-030-11051-2_125
Abstract: In air traffic control rooms, paper flight strips are more and more replaced by digital solutions. The digital systems, however, increase the workload for air traffic controllers: For instance, each voice-command must be manually inserted into the system by the controller. Recently the AcListant® project has validated that Assistant Based Speech Recognition (ABSR) can replace the manual inputs by automatically recognized voice commands. Adaptation of ABSR to different environments, however, has shown to be expensive. The Horizon 2020 funded project MALORCA (MAchine Learning Of Speech Recognition Models for Controller Assistance), proposed a more effective adaptation solution integrating a machine learning framework. As a first showcase, ABSR was automatically adapted with radar data and voice recordings for Prague and Vienna. The system reaches command recognition error rates of 0.6% (Prague) resp. 3.2% (Vienna). This paper describes the feedback trials with controllers from Vienna and Prague.
Projects Idiap
Authors Kleinert, Matthias
Helmke, Hartmut
Siol, Gerald
Ehr, heiko
Klakow, Dietrich
Singh, Mittul
Motlicek, Petr
Christian, Kern
Aneta, Cerna
Petr, Hlousek
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Total mark: 0