Design of a Future-Oriented Intelligent Multi-modal Spoken English Platform: A Study on Deep Integration of CNN-DNN-LSTM and Self-Supervised Mechanisms

With the increasing demand for efficient English speaking ability in global communication, academic testing, and remote education, conventional language learning platforms are facing limitations in providing adaptive, real-time feedback across multiple modalities. To address these challenges, an intelligent multimodal English speaking platform (IMSEP) has been developed, integrating speech and text data through a hybrid CNN-DNN-LSTM deep fusion architecture, enhanced by a self-supervised learning mechanism. The proposed platform is capable of extracting local textual features, modeling temporal speech dynamics, and performing nonlinear multimodal alignment. Furthermore, self-supervised pretraining enables the model to utilize large-scale unlabeled data, while a reinforcement learning-based adaptive feedback mechanism provides personalized learning recommendations. Experimental validation demonstrates that, compared with baseline systems, the platform achieves improvements in pronunciation accuracy (from 84–92%), user learning efficiency (by 20%), and overall satisfaction (by 15%). These results indicate the effectiveness of the proposed method in supporting data-driven, scalable, and intelligent English language education.