Multimodal Discourse Analysis from a Meta-Linguistic Perspective Applications and Challenges of Deep Learning in Bilingual Education

Abstract

With the continuous development of bilingual education, language recognition technology cannot simultaneously capture the problem exposure of different languages. Based on deep learning technology, this paper proposes a dual speech recognition evaluation model for multimodal discourse analysis in bilingual education. The end-to-end Transformer TTS model based on text prior is applied to speech synthesis by combining Transformer with Tacotron2. Focal loss function achieves the best performance in oral evaluation when γ=0.5. On this basis, a series of adaptions have been made to make Transformer better serve speech recognition synthesis scenarios. This paper analyzes the necessity of unified semantic generalization methods inside and outside the text, then delves into the cognitive principles involved in reading the text, and analyzes in detail the influence of human potential thinking and inference processes on text semantic understanding, as well as the reasons why human beings maintain high generalization ability in complex language environments, and expounds the two-stage development process from statistical learning to deep learning in natural language processing. Following the cognitive processes of human experience recall, probability analysis and sequence analysis, a two-stage modeling strategy of "pre-classification" + "enhanced classification" was proposed, which organically integrated "statistical method prior and deep learning method posterior", and designed a label interaction model based on statistical information. It was found that for the model trained with large sample data, the average BLEU value of the label interaction model (single language) increased by 0.93 and 0.56, respectively, compared with the baseline RNN Search model and the comparison model POS-NMT. Compared with the baseline model RNN Search and the comparison model POS-NMT, the label interaction model (bilingual) improved BLEU by an average of 0.69 and 0.32, respectively.