Optimization of methods for improving English cross-cultural communication skills based on deep learning and the internet of things

Abstract

Abstract

English cross-cultural communication is an essential ability in the current globalized environment, whereby successful communication between and among linguistic and cultural groups improves academic, professional and social interaction. New technologies offer the possibilities to enhance the efficiency of learning with the help of the smart, data-driven methodology. Nevertheless, traditional approaches do not tend to provide real-time feedback, situational flexibility, and individualized learning-processes, and this restricts the learners to hone spoken communication in real cross-cultural situations. Such weaknesses make it difficult to become fluent, culture sensitive, and confident in practice. To overcome such problems, this paper suggests an IoT-Social Interaction Feedback Network (IoT-SIFNet), a system that incorporates Internet of Things (IoT) wearables with deep learning models. An IoT-based system records the natural speaking data, and deep learning systems are used to detect pronunciation, tonal and cultural correctness, and offer feedback accordingly. This model observes the communication behavior under real-life conditions and dynamically optimizes practice by means of cyclic learning. The offered approach was implemented with 80 English learners involved in multicultural dialogue, which allows providing individual training modules, prompt correction, and context-dependent guidance. The experimental outcomes indicate that there is an improvement in all important metrics, such as fluency by 72%, accuracy in pronunciation by 78%, cultural sensitivity index is 0.71 (normalized 0 to 1), response time dropped by 3%, feedback efficacy enhanced by 75% and the overall ability to communicate improved by 75%. Those results prove that IoT-SIFNet is much more effective in comparison with the current solutions and show the prospects of integrating IoT and deep learning in the next-generation language-teaching.