Publication Details

Readback Error Detection by Automatic Speech Recognition to Increase ATM Safety

HELMKE Hartmut, KLEINERT Matthias, SHETTY Shruthi, OHNEISER Oliver, EHR Heiko, PRASAD Amrutha, MOTLÍČEK Petr, VESELÝ Karel, ONDŘEJ Karel, SMRŽ Pavel, HARFMANN Julia and WINDISCH Christian et al. Readback Error Detection by Automatic Speech Recognition to Increase ATM Safety. In: Proceedings of ATM 2021. on-line: Federal Aviation Administration, 2021, pp. 1-10. Available from:
Czech title
Detekce chyb zpětného čtení pomocí automatického rozpoznávání řeči pro zvýšení bezpečnosti ATM
conference paper
Helmke Hartmut (DLR)
Kleinert Matthias (DLR)
Shetty Shruthi (DLR)
Ohneiser Oliver (DLR)
Ehr Heiko (DLR)
Prasad Amrutha (IDIAP)
Motlíček Petr, Ing., Ph.D. (IDIAP)
Veselý Karel, Ing., Ph.D. (DCGM FIT BUT)
Ondřej Karel, Ing. (DCGM FIT BUT)
Smrž Pavel, doc. RNDr., Ph.D. (DCGM FIT BUT)
Harfmann Julia (NATS)
and others

Automatic Speech Recognition (ASR), Readback Error Detection, Air Traffic Control (ATC)


One of the crucial tasks of an air traffic controller (ATCo) is to evaluate pilot readbacks and to react in case of errors. Undetected readback errors, when not corrected by ATCos, can have a dramatic impact on air traffic management (ATM) safety. Although they seldomly occur, the benefits of even one prevented incident due to automatic readback error detection justifies the efforts. This, however, requires highly reliable detections, which is beyond the performance of currently available automatic speech recognition implementations. The HAAWAII project aims to achieve false alarm rates below 10% and readback error detection rates better than 50%. After performing a preliminary analysis by comparing ATCo utterances with pilot readbacks on word level, this approach proves to be very ineffective. Callsigns are abbreviated or not even pronounced, altitude and speed units are often not used, for example nineteen eight is the same as one one nine decimal eight. Therefore, the presented approach transforms recognized word sequences into so-called ATC concepts, as agreed with the ontology of the SESAR project PJ.16-04. Detecting readback errors on concept level is more reliable and robust as it also considers different forms of conveying the same semantic messages and is also more tolerant to partially misrecognized words. Nevertheless, a good recognition rate on word level is essential to correctly transform words into concepts, which will be achieved by integrating voice data from ATCo utterances and pilot readbacks with context information such as data concerning radar, flight plans, and weather. This paper presents relevant use cases, the ontology-based algorithm, and initial results regarding callsign recognition accuracy for automatic readback error detection purposes.

Proceedings of ATM 2021
14th USA/Europe Air Traffic Management Research and Development Seminar (ATM2021), on-line, US
Federal Aviation Administration
on-line, US
EID Scopus
   author = "Hartmut Helmke and Matthias Kleinert and Shruthi Shetty and Oliver Ohneiser and Heiko Ehr and Amrutha Prasad and Petr Motl\'{i}\v{c}ek and Karel Vesel\'{y} and Karel Ond\v{r}ej and Pavel Smr\v{z} and Julia Harfmann and Christian Windisch and et al.",
   title = "Readback Error Detection by Automatic Speech Recognition to Increase ATM Safety",
   pages = "1--10",
   booktitle = "Proceedings of ATM 2021",
   year = 2021,
   location = "on-line, US",
   publisher = "Federal Aviation Administration",
   language = "english",
   url = ""
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