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Real-time Controlling Dynamics Sensing in Air Traffic System.

Yi Lin1,2, Xianlong Tan3, Bo Yang4,5

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Summary

A new framework processes air traffic control (ATC) speech using automatic speech recognition (ASR) and controlling instruction understanding (CIU) for real-time dynamics. This system achieves high accuracy and efficiency, improving air traffic management.

Keywords:
ATC speechautomatic speech recognitionaverage poolingcontrolling instruction understandingdeep learninglanguage model

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Area of Science:

  • Speech Processing and Natural Language Processing
  • Artificial Intelligence in Aviation Systems
  • Real-time Data Analysis for Air Traffic Management

Background:

  • Current air traffic control (ATC) systems lack real-time processing of radiotelephony communications.
  • Extracting actionable insights from spoken ATC commands is crucial for enhancing safety and efficiency.
  • Existing speech recognition technologies face challenges in the noisy and specialized domain of ATC.

Purpose of the Study:

  • To develop a novel framework for real-time processing of ATC speech to derive controlling dynamics.
  • To convert spoken ATC commands into structured data, including controlling intent and parameters.
  • To improve the reliability and accuracy of information extraction from ATC communications.

Main Methods:

  • A pipeline integrating automatic speech recognition (ASR) and controlling instruction understanding (CIU) models.
  • A multilingual ASR model incorporating acoustic, pronunciation, and language models with 2D convolution and pooling layers.
  • A recurrent neural network-based CIU model for joint intent detection and parameter labeling, enhanced by a post-processing correction module using flight data.

Main Results:

  • The proposed framework achieved a high performance with a 4% word-error rate in ASR.
  • The system demonstrated real-time processing capabilities with an average real-time factor of 0.147.
  • Trained on real operational data from a Chinese civil aviation airport, the models proved effective.

Conclusions:

  • The developed framework successfully extracts real-time controlling dynamics from ATC speech with high accuracy and efficiency.
  • The system's performance meets the stringent requirements for real-time air traffic management applications.
  • The ASR pipeline exhibits high reusability, adaptable to different control scenarios and languages with minimal modifications.