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 [BibTeX] [Marc21]
Building Blocks of Assistant Based Speech Recognition for Air Traffic Management Applications
Type of publication: Conference paper
Citation: Kleinert_SESARINNOVATIONDAYS_2018
Publication status: Accepted
Booktitle: Conference: SESAR Innovation Days 2018
Year: 2018
Month: December
Publisher: SESARJU
Location: Salzburg, Austria
Organization: European Union, Eurocontrol
URL: https://www.sesarju.eu/sesarin...
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.
Keywords: Assistant Based Speech Recognition, Automatic Speech Recognition, Building Blocks, machine learning
Projects Idiap
MALORCA
Authors Kleinert, Matthias
Helmke, Hartmut
Ehr, heiko
Christian, Kern
Klakow, Dietrich
Motlicek, Petr
Singh, Mittul
Siol, Gerald
Added by: [UNK]
Total mark: 0
Attachments
  • Kleinert_SESARINNOVATIONDAYS_2018.pdf
Notes