Advancing Airport Tower Command Recognition: Integrating Squeeze-and-Excitation and Broadcasted Residual Learning

Accurate recognition of aviation commands is vital for flight safety and efficiency, as pilots must follow air traffic control instructions precisely. This paper addresses challenges in speech command recognition, such as noisy environments and limited computational resources, by advancing keyword s...

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Bibliographic Details
Main Authors: Lin, Yuanxi, Zhou, Tonglin, Xiao, Yang
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Air Traffic Control
Atmospheric modeling
Computational modeling
Keyword Spotting
Poles and towers
Speech recognition
Squeeze-and-Excitation
Time-frequency analysis
Traffic control
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Summary:Accurate recognition of aviation commands is vital for flight safety and efficiency, as pilots must follow air traffic control instructions precisely. This paper addresses challenges in speech command recognition, such as noisy environments and limited computational resources, by advancing keyword spotting technology. We create a dataset of standardized airport tower commands, including routine and emergency instructions. We enhance broadcasted residual learning with squeeze-and-excitation and time-frame frequency-wise squeeze-and-excitation techniques, resulting in our BC-SENet model. This model focuses on crucial information with fewer parameters. Our tests on five keyword spotting models, including BC-SENet, demonstrate superior accuracy and efficiency. These findings highlight the effectiveness of our model advancements in improving speech command recognition for aviation safety and efficiency in noisy, high-stakes environments. Additionally, BC-SENet shows comparable performance on the common Google Speech Command dataset.
ISSN:2159-1970
DOI:10.1109/IALP63756.2024.10661143