Publication Details

Automatic Processing Pipeline for Collecting and Annotating Air-Traffic Voice Communication Data

KOCOUR, M.; VESELÝ, K.; SZŐKE, I.; KESIRAJU, S.; ZULUAGA-GOMEZ, J.; BLATT, A.; PRASAD, A.; NIGMATULINA, I.; MOTLÍČEK, P.; KLAKOW, D.; TART, A.; KOLČÁREK, P.; ČERNOCKÝ, J.; CEVENINI, C.; CHOUKRI, K.; RIGAULT, M.; LANDIS, F.; SARFJOO, S. Automatic Processing Pipeline for Collecting and Annotating Air-Traffic Voice Communication Data. In Proceedings of 9th OpenSky Symposium 2021, OpenSky Network, Brussels, Belgium. Proceedings. Brussels: MDPI, 2021. p. 1-10. ISSN: 2504-3900.
Czech title
Řetězec automatického zpracování pro sběr a anotaci řečových dat komunikace v řízení letového provozu
Type
conference paper
Language
English
Authors
Kocour Martin, Ing. (DCGM)
Veselý Karel, Ing., Ph.D. (DCGM)
Szőke Igor, Ing., Ph.D. (DCGM)
Kesiraju Santosh, Ph.D. (DCGM)
ZULUAGA-GOMEZ, J.
BLATT, A.
Prasad Amrutha (DCGM)
NIGMATULINA, I.
Motlíček Petr, doc. Ing., Ph.D. (DCGM)
KLAKOW, D.
TART, A.
KOLČÁREK, P.
Černocký Jan, prof. Dr. Ing. (DCGM)
CEVENINI, C.
CHOUKRI, K.
RIGAULT, M.
LANDIS, F.
SARFJOO, S.
and others
URL
Keywords

automatic speech recognition; air traffic control; contextual adaptation; language identification; named entity recognition; opensky network

Abstract

This document describes our pipeline for automatic processing of ATCO pilot audio communication we developed as part of the ATCO2 project. So far, we collected two thousand hours of audio recordings that we either preprocessed for the transcribers or used for semi-supervised training. Both methods of using the collected data can further improve our pipeline by retraining our models. The proposed automatic processing pipeline is a cascade of many standalone components: (a) segmentation, (b) volume control, (c) signal-to-noise ratio filtering, (d) diarization, (e) speech-totext (ASR) module, (f) English language detection, (g) call-sign code recognition, (h) ATCOpilot classification and (i) highlighting commands and values. The key component of the pipeline is a speech-to-text transcription system that has to be trained with real-world ATC data; otherwise, the performance is poor. In order to further improve speech-to-text performance, we apply both semisupervised training with our recordings and the contextual adaptation that uses a list of plausible callsigns from surveillance data as auxiliary information. Downstream NLP/NLU tasks are important from an application point of view. These application tasks need accurate models operating on top of the real speech-to-text output; thus, there is a need for more data too. Creating ATC data is the main aspiration of the ATCO2 project. At the end of the project, the data will be packaged and distributed by ELDA.

Published
2021
Pages
1–10
Journal
Proceedings, vol. 2021, no. 12, ISSN 2504-3900
Proceedings
Proceedings of 9th OpenSky Symposium 2021, OpenSky Network, Brussels, Belgium
Publisher
MDPI
Place
Brussels
DOI
10.3390/engproc2021013008
EID Scopus
2-s2.0-85128550216
BibTeX 
@inproceedings{BUT176487,
  author="KOCOUR, M. and VESELÝ, K. and SZŐKE, I. and KESIRAJU, S. and ZULUAGA-GOMEZ, J. and BLATT, A. and PRASAD, A. and NIGMATULINA, I. and MOTLÍČEK, P. and KLAKOW, D. and TART, A. and KOLČÁREK, P. and ČERNOCKÝ, J. and CEVENINI, C. and CHOUKRI, K. and RIGAULT, M. and LANDIS, F. and SARFJOO, S.",
  title="Automatic Processing Pipeline for Collecting and Annotating Air-Traffic Voice Communication Data",
  booktitle="Proceedings of 9th OpenSky Symposium 2021, OpenSky Network, Brussels, Belgium",
  year="2021",
  journal="Proceedings",
  volume="2021",
  number="12",
  pages="1--10",
  publisher="MDPI",
  address="Brussels",
  doi="10.3390/engproc2021013008",
  issn="2504-3900",
  url="https://www.mdpi.com/2673-4591/13/1/8/htm"
}
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