Detail výsledku
Readback Error Detection by Automatic Speech Recognition and Understanding - Results of HAAWAII project for Isavia's Enroute Airspace
Ondřej Karel, Ing., UPGM (FIT)
SHETTY, S.
KLEINERT, M.
OHNEISER, O.
EHR, H.
ZULUAGA-GOMEZ, J.
Smrž Pavel, doc. RNDr., Ph.D., UPGM (FIT)
a další
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 the ATCo, can
have a dramatic impact on air traffic management (ATM) safety.
Although they seldom occur, the benefits of even one prevented
incident due to automatic readback error detection justify the
efforts. The HAAWAII project uses automatic speech recognition
and understanding (ASRU) to support the ATCo in this critical
task. This paper presents for readback error detection
approaches: a rule-based and a data-driven approach based on
machine learning. The combination of both detects 81% of the
readback error samples on real-life voice recordings from Isavias
en-route airspace. Proof-of-concept trials with six ATCos from
Isavia producing artificial, but challenging readback error
samples resulted in a false alarm rate of 11% and a readback error
detection rate of 80%. These results are based on Word Error
Rates of 5% for ATCos and 10% for pilots, respectively.
readback error detection, speech recognition, speech understanding, air traffic control, assistant based speech recognition, machine learning
@inproceedings{BUT193226,
author="HELMKE, H. and ONDŘEJ, K. and SHETTY, S. and KLEINERT, M. and OHNEISER, O. and EHR, H. and ZULUAGA-GOMEZ, J. and SMRŽ, P.",
title="Readback Error Detection by Automatic Speech Recognition and Understanding - Results of HAAWAII project for Isavia's Enroute Airspace",
booktitle="SESAR Innovation Days 2022",
year="2022",
pages="1--9",
address="Budapest",
url="https://www.sesarju.eu/sites/default/files/documents/sid/2022/paper_3.pdf"
}Výzkumná skupina znalostních technologií (VZ KNOT)