Publication Details

Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator

PRASAD, A.; ZULUAGA-GOMEZ, J.; MOTLÍČEK, P.; SARFJOO, S.; NIGMATULINA, I.; VESELÝ, K. Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator. Proceedings of the 12th SESAR Innovation Days. Budapest: 2022. p. 1-9.
Czech title
Technologie zpracování řeči a přirozeného jazyka pro simulátor pseudopilota
Type
conference paper
Language
English
Authors
Prasad Amrutha (DCGM)
ZULUAGA-GOMEZ, J.
Motlíček Petr, doc. Ing., Ph.D. (DCGM)
Sarfjoo Seyyed Saeed
NIGMATULINA, I.
Veselý Karel, Ing., Ph.D. (DCGM)
URL
Keywords

Machine learning, air traffic controller training, air traffic management, BERT, automatic speech recognition, speech synthesi

Abstract

This paper describes a simple yet efficient repetition- based modular system for speeding up air-traffic controllers (ATCos) training. E.g., a human pilot is still required in EUROCONTROL's ESCAPE lite simulator https:// www.eurocontrol.int/simulator/escape during ATCo training. However, this need can be substituted by an automatic system that could act as a pilot. In this paper, we aim to develop and integrate a pseudo-pilot agent into the ATCo training pipeline by merging diverse artificial intelligence (AI) powered modules. The system understands the voice communications issued by the ATCo, and, in turn, it generates a spoken prompt that follows the pilot's phraseology to the initial communication. Our system mainly relies on open-source AI tools and air traffic control (ATC) databases, thus, proving its simplicity and ease of replicability. The overall pipeline is composed of the following: (1) a submodule that receives and pre-processes the input stream of raw audio, (2) an automatic speech recognition (ASR) system that transforms audio into a sequence of words; (3) a high-level ATC- related entity parser, which extracts relevant information from the communication, i.e., callsigns and commands, and finally, (4) a speech synthesizer submodule that generates responses based on the high-level ATC entities previously extracted. Overall, we show that this system could pave the way toward developing a real proof-of-concept pseudo-pilot system. Hence, speeding up the training of ATCos while drastically reducing its overall cost.

Published
2022
Pages
1–9
Proceedings
Proceedings of the 12th SESAR Innovation Days
Place
Budapest
DOI
BibTeX
@inproceedings{BUT185193,
  author="PRASAD, A. and ZULUAGA-GOMEZ, J. and MOTLÍČEK, P. and SARFJOO, S. and NIGMATULINA, I. and VESELÝ, K.",
  title="Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator",
  booktitle="Proceedings of the 12th SESAR Innovation Days",
  year="2022",
  pages="1--9",
  address="Budapest",
  doi="10.48550/arXiv.2212.07164",
  url="https://arxiv.org/pdf/2212.07164.pdf"
}
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