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Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator

Paper ID

SIDs-2022-074

Conference

SESAR Innovation Days

Year

2022

Theme

Automatic Speech Recognition II

Project Name

Clean Sky 2 project ATCO2, SESAR 2020 ER4 project HAAWAII

Keywords:

air traffic controller training, Air Traffic Management, automatic speech recognition, BERT, Machine learning, speech synthesis

Authors

Amrutha Prasad, Juan Pablo Zuluaga Gómez, Petr Motlicek, Saeed Sarfjoo, Iuliia Nigmatulina and Karel Vesely

DOI

Project Number

884287

Project Number

864702

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.