Automatic Speech Recognition (ASR) can introduce higher levels of automation into Air Traffic Control (ATC), where spoken language is still the predominant form of communication. While ATC uses standard phraseology and a limited vocabulary, we need to adapt the speech recognition systems to local acoustic conditions and vocabularies at each airport to reach optimal performance. Due to continuous operation of ATC systems, a large and increasing amount of untranscribed speech data is available, allowing for semi-supervised learning methods to build and adapt ASR models. In this paper, we first identify the challenges in building ASR systems for specific ATC areas and propose to utilize out-of-domain data to build baseline ASR models. Then we explore different methods of data selection for adapting baseline models by exploiting the continuously increasing untranscribed data. We develop a basic approach capable of exploiting semantic representations of ATC commands. We achieve relative improvement in both word error rate (23.5%) and concept error rates (7%) when adapting ASR models to different ATC conditions in a semi-supervised manner.

Air Navigation Service Providers (ANSPs) replace paper flight strips through different digital solutions. The instructed commands from an air traffic controller (ATCos) are then available in computer readable form. However, those systems require manual controller inputs, i.e. ATCos' workload increases. The Active Listening Assistant (AcListant®) project has shown that Assistant Based Speech Recognition (ABSR) is a potential solution to reduce this additional workload. However, the development of an ABSR application for a specific targetdomain usually requires a large amount of manually transcribed audio data in order to achieve task-sufficient recognition accuracies. MALORCA project developed an initial basic ABSR system and semi-automatically tailored its recognition models for both Prague and Vienna approaches by machine learning from automatically transcribed audio data. Command recognition error rates were reduced from 7.9% to under 0.6% for Prague and from 18.9% to 3.2% for Vienna.

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