ISCA Archive Interspeech 2018
ISCA Archive Interspeech 2018

Iterative Learning of Speech Recognition Models for Air Traffic Control

Ajay Srinivasamurthy, Petr Motlicek, Mittul Singh, Youssef Oualil, Matthias Kleinert, Heiko Ehr, Hartmut Helmke

Automatic Speech Recognition (ASR) has recently proved to be a useful tool to reduce the workload of air traffic controllers leading to significant gains in operational efficiency. Air Traffic Control (ATC) systems in operation rooms around the world generate large amounts of untranscribed speech and radar data each day, which can be utilized to build and improve ASR models. In this paper, we propose an iterative approach that utilizes increasing amounts of untranscribed data to incrementally build the necessary ASR models for an ATC operational area. Our approach uses a semi-supervised learning framework to combine speech and radar data to iteratively update the acoustic model, language model and command prediction model (i.e. prediction of possible commands from radar data for a given air traffic situation) of an ASR system. Starting with seed models built with a limited amount of manually transcribed data, we simulate an operational scenario to adapt and improve the models through semi-supervised learning. Experiments on two independent ATC areas (Vienna and Prague) demonstrate the utility of our proposed methodology that can scale to operational environments with minimal manual effort for learning and adaptation.

doi: 10.21437/Interspeech.2018-1447

Cite as: Srinivasamurthy, A., Motlicek, P., Singh, M., Oualil, Y., Kleinert, M., Ehr, H., Helmke, H. (2018) Iterative Learning of Speech Recognition Models for Air Traffic Control. Proc. Interspeech 2018, 3519-3523, doi: 10.21437/Interspeech.2018-1447

  author={Ajay Srinivasamurthy and Petr Motlicek and Mittul Singh and Youssef Oualil and Matthias Kleinert and Heiko Ehr and Hartmut Helmke},
  title={{Iterative Learning of Speech Recognition Models for Air Traffic Control}},
  booktitle={Proc. Interspeech 2018},