Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator

TL;DR

This paper presents a modular AI-based pseudo-pilot system that automates voice communication in air traffic control training, aiming to accelerate training and reduce costs by replacing human pilots with an open-source AI pipeline.

Contribution

It introduces a simple, open-source, modular system integrating speech recognition, entity parsing, and speech synthesis to simulate pilot responses in ATC training environments.

Findings

System effectively understands ATC commands and generates appropriate spoken responses.

Uses open-source AI tools and ATC databases for ease of replication.

Potential to significantly reduce training costs and time for air-traffic controllers.

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 (see 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.