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As the pace of global integration increases, so does the demand for English language courses. Due to the scarcity of English-language learning resources in China, students of the language often struggle to improve their spoken English. Advances in artificial intelligence technology and language education approaches have a new era of language teaching and learning. To solve this issue, we can employ deep learning (DL) technology. The heart of verbal communication learning is speech recognition software, which is also utilized as an assessment tool. More hardware, software, and algorithms are needed to analyze speech signals because of the complexity of speech pronunciation variations, the quantity of speech signal data, the sizeable number of speech characteristic parameters, and the magnitude of speech gratitude and assessment calculation. However, it is challenging to increase the precision and speed of conventional speech recognition algorithms since they have run across previously unheard-of bottlenecks. This article focuses on examining the impact of college English's multimedia instruction in order to address these issues. The EMLP-SNN technique, which improves multilayer perceptron integration with spiking neural networks, is suggested for identifying oral English pronunciation. The results of the experiments demonstrate that the suggested algorithm can help students identify discrepancies between their pronunciation and the norm and fix pronunciation mistakes, leading to enhanced oral English learning performance.
Cheng et al. (Wed,) studied this question.