  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.
