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Enhancing Air Traffic Control Communication Systems with Integrated Automatic Speech Recognition: Models,

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Summary
This summary is machine-generated.

Automatic speech recognition (ASR) systems can significantly reduce air traffic controller workload. This review details current ASR research in air traffic control (ATC) and proposes an improved evaluation methodology for future advancements.

Keywords:
air traffic controlautomatic speech recognitionspeech communication

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Area of Science:

  • Computer Science
  • Aerospace Engineering

Background:

  • Air traffic control (ATC) relies heavily on radio-based speech communication between controllers and pilots.
  • Integrating automatic speech recognition (ASR) offers potential for workload reduction and enhanced ATC operations.

Purpose of the Study:

  • To provide a comprehensive review of current ASR applications in ATC communication systems.
  • To propose a more accurate evaluation methodology for ATC ASR systems.
  • To identify future research directions in ATC ASR.

Main Methods:

  • Literature review of existing ATC corpora, ASR models, evaluation metrics, and application scenarios.
  • Analysis of advancements in communication sensing and deep learning for ASR.
  • Development of a tailored evaluation methodology for ATC.

Main Results:

  • Overview of current research landscape in ATC ASR.
  • Proposal of an enhanced evaluation methodology considering new technologies.
  • Identification of key challenges and future research recommendations.

Conclusions:

  • ASR technology is crucial for improving ATC efficiency and safety.
  • The proposed methodology can guide researchers in developing more effective ASR systems for ATC.
  • This work serves as a roadmap for ASR research in the ATC domain.