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

TL;DR

This paper introduces BC-SENet, a novel speech command recognition model that combines squeeze-and-excitation techniques with broadcasted residual learning, significantly improving accuracy and efficiency in noisy aviation environments.

Contribution

The paper develops BC-SENet, integrating squeeze-and-excitation modules into residual learning for enhanced keyword spotting in aviation, with superior performance over existing models.

Findings

BC-SENet outperforms five benchmark models in accuracy.

The model achieves high efficiency with fewer parameters.

Effective in noisy, high-stakes environments.

Abstract

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.