TU Delft / AE / CO / CNS-ATM / Master thesis assignment:

ATC radiotelephony voice communication data mining


Assignment Description:

Current air traffic control commands between air traffic controllers (ATCo) and pilots are primarily handled through radiotelephony (RT) voice communication. At the same time, many studies have envisioned a future system where communication is digitized, or even automated.

Platforms such as LiveATC provide access to these real-time and recorded communications. This data provides us a source for deriving information from these voice communications, and thus, provides the potential knowledge for future automation.

Several research projects in the past had focused on constructing speech recognition methods to automatic transcribe ATCo voice commands. However, few leads to an open model that can be used freely. This thesis project aims at constructing an open reference dataset and a functional neural network model for ATCo speech recognition, which would be used for future ATM human-in-the-loop studies.

This project aims at combining open-source speech recognition libraries, neural networks, open air traffic data to generate an open model that can convert ATCo voice to text. Potentially, the following topics will be explored:

Requirements:

In addition to related aeronautics background knowledge, we are looking for an MSc student who:

Supervision:

  1. Dr. Junzi Sun (j.sun-1@tudelft.nl) [Daily supervisor]
  2. Prof.dr.ir Jacco Hoekstra (j.m.hoekstra@tudelft.nl)

Sample reference:

  1. Helmke, H, et al., Increased acceptance of controller assistance by automatic speech recognition. In Tenth USA/Europe Air Traffic Management Research and Development Seminar, 2013.
  2. Nguyen, V. N., & Holone, H. Possibilities, challenges and the state of the art of automatic speech recognition in air traffic control. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 9(8), 1940-1949, 2015.
  3. Kleinert, M., et al., Machine learning of controller command prediction models from recorded radar data and controller speech utterance, in 7th SESAR Innovation Days, Belgrade, 2017.
  4. Kleinert, M., et al., Semi-supervised Adaptation of Assistant Based Speech Recognition Models for different Approach Areas, in 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) (pp. 1-10). IEEE, 2018.
  5. Pellegrini, T., et al., The Airbus air traffic control speech recognition 2018 challenge: towards ATC automatic transcription and call sign detection, 2018.
  6. Chen, S. et al., Characterizing National Airspace System Operations Using Automated Voice Data Processing, ATM Seminar, 2019


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