A Multimodal English Language Teaching Framework Based on Attention Mechanisms and Speech Analysis

Abstract

The integration of multimodal data has become a transformative approach in English language learning, enabling more effective teaching by combining complementary modalities such as speech, text, and visual inputs. This study proposes a novel multimodal teaching framework grounded in attention mechanisms and advanced speech analysis techniques. The framework aims to address key challenges in multimodal integration, including feature misalignment, modality imbalance, and the lack of adaptive learning feedback. By employing attention mechanisms, the framework dynamically aligns heterogeneous features across modalities, ensuring their effective fusion into a unified representation. Speech analysis is incorporated to assess pronunciation and fluency in real time, providing personalized feedback to learners. The system adapts dynamically to individual progress, tailoring learning experiences to meet diverse needs. Experimental results demonstrated that the proposed framework achieved significant improvements over single-modality baselines, with a 9.2% increase in accuracy, an alignment score of 0.82, and a 23.6% improvement rate in learning outcomes. The attention mechanism was particularly effective in prioritizing relevant features and enhancing cross-modal alignment. Ablation studies confirmed the critical contributions of the attention mechanism and personalized feedback module to overall performance. These findings highlight the potential of the framework as a robust, scalable solution for multimodal language education.