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.

翻译：航空指令的准确识别对飞行安全与效率至关重要，飞行员必须精确遵循空中交通管制指令。本文通过推进关键词检测技术，应对语音指令识别中的挑战，如噪声环境和有限的计算资源。我们创建了一个包含常规与应急指令的标准化机场塔台指令数据集。我们通过融合Squeeze-and-Excitation与时频维度Squeeze-and-Excitation技术，增强了广播残差学习方法，由此提出了BC-SENet模型。该模型以更少的参数聚焦关键信息。我们在包括BC-SENet在内的五个关键词检测模型上的测试表明，其具有卓越的准确性与效率。这些发现凸显了我们的模型改进在提升噪声高风险环境下航空安全与效率的语音指令识别方面的有效性。此外，BC-SENet在常见的Google Speech Command数据集上也表现出相当的性能。