Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator

• URL: http://arxiv.org/abs/2212.07164v1
• Date: Wed, 14 Dec 2022 11:34:59 GMT
• Title: Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator
• Authors: Amrutha Prasad, Juan Zuluaga-Gomez, Petr Motlicek, Saeed Sarfjoo, Iuliia Nigmatulina, Karel Vesely
• Abstract summary: 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 EURO's ESCAPE lite simulator (see https://www.eurocontrol.int/simulator/escape) during ATCo training. This need can be substituted by an automatic system that could act as a pilot.
• Score: 0.5480546613836199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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