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Updated: Sep 19, 2025

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Exploring Contextual Knowledge-Enhanced Speech Recognition in Air Traffic Control Communication: A Comparative Study.

Dongyue Guo, Shiyu Zhang, Jianwei Zhang

    IEEE Transactions on Neural Networks and Learning Systems
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    Summary
    This summary is machine-generated.

    Integrating contextual knowledge significantly improves automatic speech recognition (ASR) for air traffic control (ATC) spoken instructions. A new model, CATCNet, achieves high accuracy, overcoming challenges like noise and fast speech in ATC communications.

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    Area of Science:

    • Speech Recognition
    • Artificial Intelligence
    • Aerospace Engineering

    Background:

    • Automatic Speech Recognition (ASR) faces challenges in accurately recognizing named entities in air traffic control (ATC) communications, limiting the reliability of ASR applications.
    • Integrating contextual knowledge into ASR is a promising approach to enhance performance by leveraging domain-specific named entities.
    • Existing research lacks comprehensive benchmarks for evaluating different contextual ASR approaches in the ATC domain.

    Purpose of the Study:

    • To conduct a comprehensive comparative study of contextual ASR approaches for the ATC domain.
    • To introduce and evaluate a novel contextual ASR model, CATCNet, designed for ATC-specific challenges.
    • To establish a benchmark for evaluating ASR performance in ATC based on practical requirements.

    Main Methods:

    • Comparison of several typical contextual ASR approaches within the ATC domain.
    • Development and implementation of a novel contextual ASR model (CATCNet) addressing ATC-specific issues like limited resources, fast speech, and noise.
    • Proposal of specific evaluation metrics tailored to ATC requirements.
    • Extensive experiments conducted on two real-world ATC speech corpora (ATCSpeech and C-ATCSpeech).

    Main Results:

    • Integrating context knowledge demonstrably improves the recognition accuracy of named entities in ATC spoken instructions.
    • The proposed CATCNet model outperformed all baseline models in the comparative study.
    • CATCNet achieved instruction recognition accuracy (IRA) of 80.0% on the ATCSpeech corpus and 86.54% on the C-ATCSpeech corpus.

    Conclusions:

    • Contextual knowledge integration is a vital strategy for enhancing ASR performance in the demanding ATC environment.
    • The novel CATCNet model offers a robust and effective solution for improving ASR accuracy in air traffic control.
    • This research provides a valuable benchmark and a practical solution for ATC-related ASR applications, overcoming current performance bottlenecks.