## 5 Conclusions and Outlook  
This study, within the background of AI and emotion evaluation technology, integrates multimodal information and audio-based video generation technology into the evaluation system of emotional expressions on the basis of the existing spoken emotion evaluation. Through data generation, multimodal information fusion, and deep learning, this paper achieves emotion feature extraction and automatic evaluation for emotion expression in second language learning. This method significantly enhances the existing evaluation system, addresses the limitations of pure audio evaluation, and provides a more detailed analysis of learners' emotional expression. This study designed a method for integrating multimodal information and applied it for the first time in a language emotion evaluation system. This method can provide more accurate emotional feedback and improvement approaches, providing targeted guidance for students to enhance their expression ability in a second language. As artificial intelligence technology further develops, we expect to optimize and extend the methodology of this study. Future research will further refine and fine-tune the emotion analysis model to enhance its ability in dealing with more complex environments and subtle emotional expressions, in order to better serve the evaluation of the emotional expressions of language learners. Meanwhile, continuous emotion evaluation according \begin{table}
\begin{tabular}{c c} \hline \hline
**Modality Settings** & **MSE\(\downarrow\)** \\ \hline A & 0.832 \\ V & 2.946 \\ A+T & 0.841 \\ A+V & 0.828 \\ T+V & 2.867 \\ Ours (A+T+V) & **0.811** \\ \hline \hline \end{tabular}  
In the table, “A” represents the audio modality, “T” represents the text modality, and “V” represents the video modality. Optimal values are bolded in the table.  
\end{table} 
Table 6: Ablation study results of emotion evaluation network under different modal settings. to time change should also be the direction of subsequent work. Finally, the study of the robustness of emotion evaluation for extreme data is also an important part of future work. This study, as a typical case of the Shaanxi University of Science and Technology's Large Language Model supporting teaching, provides a strong example and reference for future educational technology innovation. Conceptualization, L.W. ; Methodology, Y.Q. and H.C.; Software, J.Y. ; Formal analysis, Y.Q. ; Resources, L.W. and J.L. ; Data curation, J.Y. ; Writing--original draft, J.Y., L.W., Y.Q. and H.C.; Writing--review and editing, J.L. ; Project administration, J.L. ; Funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Internationalization of Education and Teaching Reform Research Project of the Shaanxi University of Science and Technology grant number GJ22 YB09, the Shaanxi University of Science and Technology 2023 General Projects for University-Level Educational Reforms grant number 23Y080, and the National Natural Science Foundation of China (NSFC) grant number 62306172. The APC was funded by 23Y080. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of School of Culture and Education, Shaanxi University of Science and Technology (protocol code 20240301 and date of approval is 6 January 2024). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The raw data supporting the conclusions of this article will be made available by the authors on request. Herein, our research team would like to express sincere gratitude for the support and assistance provided by the following projects to this thesis. We are thankful for the National Natural Science Foundation of China (NSFC) (Project No. 62306172), whose funding has facilitated the smooth progress of our research.