Artificial Intelligence-Driven Strategies for Optimizing English Speaking Teaching Models in Vocational Undergraduate Programs

Dan Liu
Nanchang Vocational University, China

Abstract:

In the context of accelerating the transformation of vocational undergraduate education, spoken English teaching needs to break through the bottlenecks faced by the traditional model, such as low resource suitability, delayed feedback, and a single structure of training tasks. To this end, the study constructs an intelligent English speaking teaching system based on the "cloud-edge-end" fusion framework, which integrates the core modules of speech recognition, semantic parsing, voice feature analysis and personalized recommendation, and relies on the BERT, CNN-XLSTM, BiLSTM, and TransLSTM to provide the teaching of English as a foreign language. The system integrates core modules such as speech recognition, semantic parsing, speech feature analysis and personalized recommendation, and relies on deep learning technologies such as BERT, CNN-BiLSTM, Transformer, etc., to form a dynamic teaching process in stages of "diagnosis before class, collaboration in class, and reinforcement after class". A 4-week comparison experiment was conducted in a real teaching environment, and the results show that the experimental group significantly outperforms the control group in multi-dimensional indexes such as F0 fluctuation stability (from ±57.2Hz to ±31.5Hz), STOI clarity score (improved by 17.4%), sentence complexity, and dialog coherence; and the Pearson's correlation coefficient between the system score and the teacher's score reaches 0.812, validating the assessment of the model. The Pearson's correlation coefficient between the system score and the teacher's score is 0.812, which confirms the reliability of the assessment. The study not only proposes a task construction and competence stratification mechanism that is suitable for vocational scenarios, but also realizes the intelligent matching of teaching tasks and learner states, which improves the response speed of teaching and the accuracy of learning paths. The results can provide a set of optimization paradigms for vocational undergraduate spoken language teaching mode with technical feasibility and practical promotion value.