Kleinert, MatthiasHelmke, HartmutEhr, heikoChristian, KernKlakow, DietrichMotlicek, PetrSingh, MittulSiol, Gerald2019-01-222019-01-222019-01-222018https://infoscience.epfl.ch/handle/20.500.14299/153618In 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.Assistant Based Speech RecognitionAutomatic Speech RecognitionBuilding Blocksmachine learningtext::conference output::conference paper not in proceedings