Speech Command Recognition (SCR), which deals with identification of short uttered speech commands, is crucial for various applications, including IoT devices and assistive technology. Despite the promise shown by Convolutional Neural Networks (CNNs) in SCR tasks, their efficacy relies heavily on hyper-parameter selection, which is typically laborious and time-consuming when done manually. This paper introduces a hyper-parameter selection method for CNNs based on the Differential Evolution (DE) algorithm, aiming to enhance performance in SCR tasks. Training and testing with the Google Speech Command (GSC) dataset, the proposed approach showed effectiveness in classifying speech commands. Moreover, a comparative analysis with Genetic Algorithm based selections and other deep CNN (DCNN) models highlighted the efficiency of the proposed DE algorithm in hyper-parameter selection for CNNs in SCR tasks.

翻译：语音命令识别（SCR）涉及对短时语音指令的识别，对于物联网设备及辅助技术等应用至关重要。尽管卷积神经网络（CNN）在SCR任务中展现出潜力，但其性能高度依赖于超参数选择，而人工调参通常繁琐且耗时。本文提出了一种基于差分进化（DE）算法的CNN超参数选择方法，旨在提升SCR任务性能。通过使用Google语音命令（GSC）数据集进行训练与测试，该方法和有效实现了语音命令分类。此外，与基于遗传算法的选择方法及其他深度CNN（DCNN）模型的比较分析，凸显了所提DE算法在SCR任务CNN超参数选择中的效率。