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Speech Command Recognition (SCR), which deals with identification of shortuttered speech commands, is crucial for various applications, including IoTdevices and assistive technology. Despite the promise shown by ConvolutionalNeural Networks (CNNs) in SCR tasks, their efficacy relies heavily onhyper-parameter selection, which is typically laborious and time-consuming whendone manually. This paper introduces a hyper-parameter selection method forCNNs based on the Differential Evolution (DE) algorithm, aiming to enhanceperformance in SCR tasks. Training and testing with the Google Speech Command(GSC) dataset, the proposed approach showed effectiveness in classifying speechcommands. Moreover, a comparative analysis with Genetic Algorithm basedselections and other deep CNN (DCNN) models highlighted the efficiency of theproposed DE algorithm in hyper-parameter selection for CNNs in SCR tasks.

Differential Evolution Algorithm based Hyper-Parameters Selection of Convolutional Neural Network for Speech Command Recognition