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

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

We present an MatchboxNet - an end-to-end neural network for speech command
recognition. MatchboxNet is a deep residual network composed from blocks of 1D
time-channel separable convolution, batch-normalization, ReLU and dropout
layers. MatchboxNet reaches state-of-the-art accuracy on the Google Speech
Commands dataset while having significantly fewer parameters than similar
models. The small footprint of MatchboxNet makes it an attractive candidate for
devices with limited computational resources. The model is highly scalable, so
model accuracy can be improved with modest additional memory and compute.
Finally, we show how intensive data augmentation using an auxiliary noise
dataset improves robustness in the presence of background noise.

MatchboxNet 是一个端到端的神经网络，用于语音命令识别，在较少的参数下达到了与类似模型相当的准确率，对于计算资源有限的设备来说是一个非常有吸引力的候选模型。通过使用辅助噪声数据进行数据增强，可以提高模型在背景噪声存在时的鲁棒性。

MatchboxNet：用于语音命令识别的 1D 时域通道可分离卷积神经网络架构

MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network  Architecture for Speech Commands Recognition

This paper introduces a convolutional recurrent network with attention for
speech command recognition. Attention models are powerful tools to improve
performance on natural language, image captioning and speech tasks. The
proposed model establishes a new state-of-the-art accuracy of 94.1% on Google
Speech Commands dataset V1 and 94.5% on V2 (for the 20-commands recognition
task), while still keeping a small footprint of only 202K trainable parameters.
Results are compared with previous convolutional implementations on 5 different
tasks (20 commands recognition (V1 and V2), 12 commands recognition (V1), 35
word recognition (V1) and left-right (V1)). We show detailed performance
results and demonstrate that the proposed attention mechanism not only improves
performance but also allows inspecting what regions of the audio were taken
into consideration by the network when outputting a given category.

本文介绍了一个使用卷积循环神经网络与注意力机制的语音指令识别模型，其创造了 94.1%（V1）和 94.5%（V2）的分类准确率，是同类模型中的新记录，并且该模型仅有 202K 个可以训练的参数，同时其提出的注意力机制不仅提高了性能，还允许可视化网络处理输入音频时所参考的区域。