Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator

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.