Publication result detail

Readback Error Detection by Automatic Speech Recognition and Understanding - Results of HAAWAII project for Isavia's Enroute Airspace

HELMKE, H.; ONDŘEJ, K.; SHETTY, S.; KLEINERT, M.; OHNEISER, O.; EHR, H.; ZULUAGA-GOMEZ, J.; SMRŽ, P.

Original Title

Readback Error Detection by Automatic Speech Recognition and Understanding - Results of HAAWAII project for Isavia's Enroute Airspace

English Title

Readback Error Detection by Automatic Speech Recognition and Understanding - Results of HAAWAII project for Isavia's Enroute Airspace

Type

Paper in proceedings outside WoS and Scopus

Original Abstract

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. 

English abstract

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. 

Keywords

readback error detection, speech recognition, speech understanding, air traffic control, assistant based speech recognition, machine learning

Key words in English

readback error detection, speech recognition, speech understanding, air traffic control, assistant based speech recognition, machine learning

Authors

HELMKE, H.; ONDŘEJ, K.; SHETTY, S.; KLEINERT, M.; OHNEISER, O.; EHR, H.; ZULUAGA-GOMEZ, J.; SMRŽ, P.

Released

05.12.2022

Location

Budapest

Book

SESAR Innovation Days 2022

Pages from

1

Pages to

9

Pages count

9

URL

https://www.sesarju.eu/sites/default/files/documents/sid/2022/paper_3.pdf

Full text in the Digital Library

http://hdl.handle.net/

BibTex

@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"
}