Result 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.
Type
conference paper
Language
English
Authors
Prasad Amrutha
ZULUAGA-GOMEZ, J.
Motlíček Petr, doc. Ing., Ph.D., DCGM (FIT)
Sarfjoo Seyyed Saeed
NIGMATULINA, I.
Veselý Karel, Ing., Ph.D., DCGM (FIT)
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.

Keywords

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

URL
Published
2022
Pages
1–9
Proceedings
Proceedings of the 12th SESAR Innovation Days
Conference
12th SESAR Innovation Days
Place
Budapest
DOI
10.48550/arXiv.2212.07164
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"
}
Files
Projects
Automatic collection and processing of voice data from air-traffic communications, EU, Horizon 2020, start: 2019-11-01, end: 2022-02-28, completed
HAAWAII - Highly Automated Air Traffic Controller Workstations with Artificial Intelligence Integration, EU, Horizon 2020, H2020-SESAR-2019-2, start: 2020-06-01, end: 2022-11-30, completed
Research groups
Speech Data Mining Research Group BUT Speech@FIT (RG SPEECH)
Departments
Department of Computer Graphics and Multimedia (DCGM)
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