%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Ohneiser_INTERSPEECH_2021,
         author = {Ohneiser, Oliver and Sarfjoo, Seyyed Saeed and Helmke, Hartmut and Shetty, Shruthi and Motlicek, Petr and Kleinert, Matthias and Ehr, heiko and {\v S}ar{\={u}}nas Murauskas},
       keywords = {Air traffic control, command recognition rate, speech recognition, speech understanding, tower utterances},
       projects = {HAAWAII, Idiap},
          title = {Robust Command Recognition for Lithuanian Air Traffic Control Tower Utterances},
      booktitle = {Interspeech},
           year = {2021},
       abstract = {The maturity of automatic speech recognition (ASR) systems at controller working positions is currently a highly relevant technological topic in air traffic control (ATC). However, ATC service providers are less interested in pure word error rate (WER). They want to see benefits of ASR applications for ATC. Such applications transform recognized word sequences into semantic meanings, i.e., a number of related concepts such as callsign, type, value, unit, etc., which are combined to form commands. Digitized concepts or recognized commands can enter ATC systems based on an ontology for utterance annotation agreed between European ATC stakeholders. Command recognition (CR) has already been performed in approach control. However, spoken utterances of tower controllers are longer, include more free speech, and contain other command types than in approach. An automatic CR rate of 95.8\% is achievable on perfect word recognition, i.e., manually transcribed audio recordings (gold transcriptions), taken from Lithuanian controllers in a multiple remote tower environment. This paper presents CR results for various speech-to-text models with different WERs on tower utterances. Although WERs were around 9\%, we achieve CR rates of 85\%. CR rates only slightly decrease with higher WERs, which enables to bring ASR applications closer to operational ATC environment.},
            pdf = {https://publications.idiap.ch/attachments/papers/2021/Ohneiser_INTERSPEECH_2021.pdf}
}