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<p>Enhancing English cross-cultural communication is essential in today's interconnected world. Leveraging advancements in deep learning and the Internet of Things (IoT) offers promising pathways to improve communication effectiveness. However, existing methods often lack real-time adaptability, ignore non-verbal cues like gestures and tone, and fail to offer personalized cultural training, leading to limited effectiveness in diverse social contexts. To address these challenges, this paper proposes an IoT-Enabled Context-Aware Deep Neural Network (IoT-DNN) framework that analyzes real-time IoT data such as speech tone, facial expressions, and gestures to dynamically adapt and enhance English language and cross-cultural communication skills. The proposed method delivers AI-driven, personalized language and cultural modules, offering learners context-sensitive feedback. This framework is utilized through wearable IoT devices and smart environments, which collect continuous multimodal data and trigger real-time interventions for improved interaction accuracy. Findings reveal that the IoT-DNN model significantly improves communication fluency, cultural sensitivity, and contextual understanding among users, outperforming traditional static training methods. The proposed system offers a scalable and intelligent solution to bridge communication gaps in multilingual and multicultural environments. The proposed method achieves the following improvements: fluency by 85%, cultural sensitivity score by 80%, gesture accuracy by 84%, speech tone adaptability by 82%, latency by 480&#xa0;ms, and contextual rating by 83%.</p>

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Optimization of English cross-cultural communication ability improvement method based on deep learning and Internet of Things

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月5日

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  • Defeng Ma

摘要

Enhancing English cross-cultural communication is essential in today's interconnected world. Leveraging advancements in deep learning and the Internet of Things (IoT) offers promising pathways to improve communication effectiveness. However, existing methods often lack real-time adaptability, ignore non-verbal cues like gestures and tone, and fail to offer personalized cultural training, leading to limited effectiveness in diverse social contexts. To address these challenges, this paper proposes an IoT-Enabled Context-Aware Deep Neural Network (IoT-DNN) framework that analyzes real-time IoT data such as speech tone, facial expressions, and gestures to dynamically adapt and enhance English language and cross-cultural communication skills. The proposed method delivers AI-driven, personalized language and cultural modules, offering learners context-sensitive feedback. This framework is utilized through wearable IoT devices and smart environments, which collect continuous multimodal data and trigger real-time interventions for improved interaction accuracy. Findings reveal that the IoT-DNN model significantly improves communication fluency, cultural sensitivity, and contextual understanding among users, outperforming traditional static training methods. The proposed system offers a scalable and intelligent solution to bridge communication gaps in multilingual and multicultural environments. The proposed method achieves the following improvements: fluency by 85%, cultural sensitivity score by 80%, gesture accuracy by 84%, speech tone adaptability by 82%, latency by 480 ms, and contextual rating by 83%.