Robot Translation Based on Computer Vision for Cultural Psychology of English Culture Education

Abstract

Individuals use language in a wide range of contexts. It is a major part of the culture. Teaching students how to speak English in a different manner requires adopting cultural attitudes and behaviors. This learning style has a tremendous sense of belonging, community, and intent. In addition, it motivates learners to create a difference in their neighborhoods and communities around the world. A simple way to incorporate culture into the curriculum is to use the abilities and narratives of the wider community. Multilingual classrooms present an incredible task for English teachers because of the students’ wide range of linguistic backgrounds. Because they are afraid of committing mistakes, the students in multilingual classrooms lack self-confidence to communicate in English. Therefore, in this paper, Robot Interaction for Social Cultural Education (RI-SCE) method is proposed to overcome the challenges mentioned above. It uses Deep Machine language and Artificial Intelligence to interact with robots-based computer vision for cultural psychology of English cultural education. As a result, the simulation shows the importance of robot translation in performance, accuracy, efficiency, security, and flexibility compared to the other available models. The model proposed here achieves standard accuracy of 95.2%.