IDEAS home Printed from https://ideas.repec.org/a/eee/jaitra/v95y2021ics0969699721000727.html
   My bibliography  Save this article

Prediction and extraction of tower controller commands for speech recognition applications

Author

Listed:
  • Ohneiser, Oliver
  • Helmke, Hartmut
  • Shetty, Shruthi
  • Kleinert, Matthias
  • Ehr, Heiko
  • Murauskas, Å arÅ«nas
  • Pagirys, Tomas

Abstract

Air traffic controllers' (ATCos) workload often is a limiting factor for air traffic capacity. Thus, electronic support systems intend to reduce ATCos' workload. Automatic speech recognition can extract controller command elements from verbal clearances to deliver automatic input for air traffic control systems, thereby avoiding manual input. Assistant Based Speech Recognition (ABSR) with high command recognition rates and low error rates has proven to dramatically reduce ATCos’ workload and increase capacity in approach scenarios. However, ABSR needs accurate hypotheses on expected commands and accurate extractions of command annotations from utterance transcriptions to achieve the required performance. Based on the experience of implementation for approach control, a hypotheses generator and a command extractor have been developed for speech recognition applications regarding tower control communication to face current and future challenges in the aerodrome environment. Three human-in-the-loop multiple remote tower simulation studies were performed with 16 ATCos from Hungary, Lithuania, and Finland at DLR Braunschweig from 2017 to 2019. Roughly 100 h of speech with corresponding radar data were recorded. Around 6000 speech utterances resulting in 16,000 commands have been manually transcribed and annotated. Some parts of the data have been used for training prediction models and command extraction algorithms. Other parts were used for evaluation of command prediction and command extraction. The automatic command extractor achieved a command extraction rate of 96.7%. The hypotheses generator showed operational feasibility with a sufficiently low command prediction error rate of 7.3%.

Suggested Citation

  • Ohneiser, Oliver & Helmke, Hartmut & Shetty, Shruthi & Kleinert, Matthias & Ehr, Heiko & Murauskas, Å arÅ«nas & Pagirys, Tomas, 2021. "Prediction and extraction of tower controller commands for speech recognition applications," Journal of Air Transport Management, Elsevier, vol. 95(C).
  • Handle: RePEc:eee:jaitra:v:95:y:2021:i:c:s0969699721000727
    DOI: 10.1016/j.jairtraman.2021.102089
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969699721000727
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jairtraman.2021.102089?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Skaltsas, Gerasimos & Rakas, Jasenka & Karlaftis, Matthew G., 2013. "An analysis of air traffic controller-pilot miscommunication in the NextGen environment," Journal of Air Transport Management, Elsevier, vol. 27(C), pages 46-51.
    2. Tobaruela, Gonzalo & Schuster, Wolfgang & Majumdar, Arnab & Ochieng, Washington Y. & Martinez, Luis & Hendrickx, Peter, 2014. "A method to estimate air traffic controller mental workload based on traffic clearances," Journal of Air Transport Management, Elsevier, vol. 39(C), pages 59-71.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Hui-Hua & Chang, Yu-Hern & Chou, Yi-Hui, 2023. "Subjective measures of communication errors between pilots and air traffic controllers," Journal of Air Transport Management, Elsevier, vol. 112(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yun Wang & Xuedong Yan & Yu Zhou & Qingwan Xue & Li Sun, 2017. "Individuals’ Acceptance to Free-Floating Electric Carsharing Mode: A Web-Based Survey in China," IJERPH, MDPI, vol. 14(5), pages 1-24, May.
    2. Li, Max Z. & Ryerson, Megan S., 2019. "Reviewing the DATAS of aviation research data: Diversity, availability, tractability, applicability, and sources," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 111-130.
    3. Bongo, Miriam F. & Alimpangog, Kissy Mae S. & Loar, Jennifer F. & Montefalcon, Jason A. & Ocampo, Lanndon A., 2018. "An application of DEMATEL-ANP and PROMETHEE II approach for air traffic controllers’ workload stress problem: A case of Mactan Civil Aviation Authority of the Philippines," Journal of Air Transport Management, Elsevier, vol. 68(C), pages 198-213.
    4. Yang, Hui-Hua & Chang, Yu-Hern & Chou, Yi-Hui, 2023. "Subjective measures of communication errors between pilots and air traffic controllers," Journal of Air Transport Management, Elsevier, vol. 112(C).
    5. Bauranov, Aleksandar & Rakas, Jasenka, 2024. "Bayesian network model of aviation safety: Impact of new communication technologies on mid-air collisions," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    6. Ibáñez-Gijón, Jorge & Travieso, David & Navia, José A. & Montes, Aitor & Jacobs, David M. & Frutos, Patricia L., 2023. "Experimental validation of COMETA model of mental workload in air traffic control," Journal of Air Transport Management, Elsevier, vol. 108(C).
    7. Kearney, Peter & Li, Wen-Chin, 2018. "Multiple remote tower for Single European Sky: The evolution from initial operational concept to regulatory approved implementation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 15-30.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jaitra:v:95:y:2021:i:c:s0969699721000727. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/journal-of-air-transport-management/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.