%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Kleinert_DASC2018_2018,
         author = {Kleinert, Matthias and Helmke, Hartmut and Siol, Gerald and Ehr, heiko and Aneta, Cerna and Christian, Kern and Klakow, Dietrich and Motlicek, Petr and Oualil, Youssef and Singh, Mittul and Srinivasamurthy, Ajay},
       keywords = {Assistant Based Speech Recognition, Automatic Speech Recognition, Command Prediction Model, machine learning, unsupervised learning},
       projects = {Idiap, MALORCA},
          month = sep,
          title = {Semi-supervised Adaptation of Assistant Based Speech Recognition Models for different Approach Areas},
      booktitle = {37th AIAA/IEEE Digital Avionics Systems Conference},
           year = {2018},
       location = {London},
   organization = {AIAA/IEEE},
           note = {The best paper award in cathegory "ST-B: Human Factors & Performance for Aerospace Applications" (http://2018.dasconline.org/pages/award-winners)},
            url = {http://www. dasconline.org},
       abstract = {Air Navigation Service Provider (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{\textregistered}) 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 target-domain 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 approach 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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2018/Kleinert_DASC2018_2018.pdf}
}