CONF Srinivasamurthy_INTERSPEECH2018_2018/IDIAP Iterative Learning of Speech Recognition Models for Air Traffic Control Srinivasamurthy, Ajay Motlicek, Petr Singh, Mittul Oualil, Youssef Kleinert, Matthias Ehr, heiko Helmke, Hartmut Air traffic control Iterative learning semi-supervised learning speech recognition EXTERNAL http://publications.idiap.ch/attachments/papers/2019/Srinivasamurthy_INTERSPEECH2018_2018.pdf PUBLIC ISCA - Proceedings of Interspeech 2018 Hyderabad, India 2018 3519-3523 10.21437/Interspeech.2018-1447 doi 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.