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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 incrementallydoi:10.21437/interspeech.2018-1447 dblp:conf/interspeech/Srinivasamurthy18 fatcat:r4mre62oene63ezyddfgvaeyxe