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With the acceleration of global integration, the demand for English instruction is increasingly rising. On the other hand, Chinese English learners struggle to learn spoken English due to the limited English learning environment and teaching conditions in China. The advancement of artificial intelligence technology and the advancement of language teaching and learning techniques have ushered in a new era of language learning and teaching. Deep learning technology makes it possible to solve this problem. Speech recognition and assessment technology are at the heart of language learning, and speech recognition technology is the foundation. Because of the complex changes in speech pronunciation, a large amount of speech signal data, the high dimension of speech characteristic parameters, and a large amount of speech recognition and evaluation computation, the large volume of speech signal processing requires higher requirements of hardware and software resources and algorithms. However, traditional speech recognition algorithms, such as dynamic time-warped algorithms, hidden Markov models, and artificial neural networks, have their advantages and disadvantages. They have encountered unprecedented bottlenecks, so it is difficult to improve their accuracy and speed. To solve these problems, this paper focuses on evaluating the multimedia teaching effect of college English. A multilevel residual convolutional neural network algorithm for oral English pronunciation recognition is proposed based on a deep convolutional neural network. The experiments show that our algorithm can assist learners in identifying inconsistencies between their pronunciation and standard pronunciation and correcting pronunciation errors, resulting in improved oral English learning performance.

Evaluation Model of College English Multimedia Teaching Effect Based on Deep Convolutional Neural Networks