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.

航空命令的准确识别对飞行安全和效率至关重要，本文通过改进关键词检测技术来解决语音命令识别中的挑战，如嘈杂环境和有限的计算资源。我们创建了一个标准化的机场塔命令数据集，改进了广播剩余学习，并结合挤压-激发和时间帧频率-挤压-激发技术，从而得到了我们的BC-SENet模型。我们对包括BC-SENet在内的五种关键词检测模型进行了测试，结果显示出更高的准确性和效率。这些发现突显了我们模型改进在嘈杂、高风险环境中提高语音命令识别的有效性，此外，BC-SENet在常见的Google Speech Command数据集上也表现出可比较的性能。

推进机场塔台指令识别：集成挤压-激励和广播残差学习