Advancing Airport Tower Command Recognition: Integrating Squeeze-and-Excitation and Broadcasted Residual Learning
It addresses flight safety and efficiency by improving recognition of airport tower commands, though it appears incremental as it builds on existing keyword spotting and residual learning techniques.
This paper tackles the problem of speech command recognition in noisy aviation environments by developing the BC-SENet model, which integrates squeeze-and-excitation and broadcasted residual learning to achieve superior accuracy and efficiency with fewer parameters, as demonstrated in tests against five keyword spotting models.
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.