<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the development of the times and the continuous innovation of modern education technology, the multimodal theory is developing deeply in the field of education. The first step in this study is to use the optimization principle to select modes that meet the needs of English translation teaching and then develop a multimodal analysis framework for external communication that incorporates multimedia communication methods. Then, we constructed an emotion recognition model for English translation teaching by using speech recognition and expression recognition combined with convolutional neural networks for the students’ classroom situation and attitude and carried out the empirical analysis. The recognition accuracy of the emotion recognition system constructed in this paper is 86.4%. In the multimodal analysis of English translation teaching, the teacher who uses the linguistic modality type for the most number of times 387 times, and the teacher who uses the mixed modality for the most number of times 271 times, the students have the best classroom status. It has been stable at about 5 points, which is a “more positive” state. The students in the class had the highest level of motivation. Through the analysis, it is found that multimodal teaching can effectively improve the students’ status in class and keep their attention, which verifies the appropriateness of the external communication path combined with multimedia communication in multimodal English translation teaching. It provides a practical path for the development of English translation teaching.</p>
</abstract>

With the development of the times and the continuous innovation of modern education technology, the multimodal theory is developing deeply in the field of education. The first step in this study is to use the optimization principle to select modes that meet the needs of English translation teaching and then develop a multimodal analysis framework for external communication that incorporates multimedia communication methods. Then, we constructed an emotion recognition model for English translation teaching by using speech recognition and expression recognition combined with convolutional neural networks for the students’ classroom situation and attitude and carried out the empirical analysis. The recognition accuracy of the emotion recognition system constructed in this paper is 86.4%. In the multimodal analysis of English translation teaching, the teacher who uses the linguistic modality type for the most number of times 387 times, and the teacher who uses the mixed modality for the most number of times 271 times, the students have the best classroom status. It has been stable at about 5 points, which is a “more positive” state. The students in the class had the highest level of motivation. Through the analysis, it is found that multimodal teaching can effectively improve the students’ status in class and keep their attention, which verifies the appropriateness of the external communication path combined with multimedia communication in multimodal English translation teaching. It provides a practical path for the development of English translation teaching.

Research on the Path of English Translation Teaching and External Communication Based on the Multimodal Analysis Method

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the development of the times and the continuous innovation of modern education technology, the multimodal theory is developing deeply in the field of...

Research on the Path of English Translation Teaching and External Communication Based on the Multimodal Analysis Method

Publicado en línea: 22 may 2024

Páginas: -

Recibido: 19 feb 2024

Aceptado: 24 abr 2024

DOI: https://doi.org/10.2478/amns-2024-1143

Palabras clave
Multimodal, Speech recognition, Expression recognition, Convolutional neural, English translation teaching

© 2024 Cui Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Path of English Translation Teaching and External Communication Based on the Multimodal Analysis Method

Publicado en línea: 22 may 2024

Páginas: -

Recibido: 19 feb 2024

Aceptado: 24 abr 2024

DOI: https://doi.org/10.2478/amns-2024-1143

Palabras claveMultimodal, Speech recognition, Expression recognition, Convolutional neural, English translation teaching

© 2024 Cui Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Multimodal, Speech recognition, Expression recognition, Convolutional neural, English translation teaching