Publication Details
Readback Error Detection by Automatic Speech Recognition to Increase ATM Safety
KLEINERT, M.
SHETTY, S.
OHNEISER, O.
EHR, H.
Prasad Amrutha (DCGM)
Motlíček Petr, doc. Ing., Ph.D. (DCGM)
Veselý Karel, Ing., Ph.D. (DCGM)
Ondřej Karel, Ing. (FIT)
Smrž Pavel, doc. RNDr., Ph.D. (DCGM)
HARFMANN, J.
WINDISCH, C.
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.
@inproceedings{BUT175857,
author="HELMKE, H. and KLEINERT, M. and SHETTY, S. and OHNEISER, O. and EHR, H. and PRASAD, A. and MOTLÍČEK, P. and VESELÝ, K. and ONDŘEJ, K. and SMRŽ, P. and HARFMANN, J. and WINDISCH, C.",
title="Readback Error Detection by Automatic Speech Recognition to Increase ATM Safety",
booktitle="Proceedings of ATM Seminar",
year="2021",
pages="1--10",
publisher="EUROPEAN ORGANISATION FOR THE SAFETY OF AIR NAVIGATION",
address="on-line",
url="https://drive.google.com/file/d/1N3o831BIAURA0GYTHkWmIpj9aYmMdu8A/view"
}