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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Multimedia English teaching analysis based on deep learning speech enhancement algorithm and robust expression positioning
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Multimedia English teaching analysis based on deep learning speech enhancement algorithm and robust expression positioning

机译：基于深度学习语音增强算法和鲁棒表达定位的多媒体英语教学分析

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摘要

In multimedia English teaching, learners face such an indifferent computer screen without emotion and feel the fun of interaction and emotional stimulation, which will cause resentment and affect the learner's learning effect. In order to improve the efficiency of multimedia English teaching, aiming at the lack of emotion in multimedia English education, this study proposes an intelligent network teaching system model based on deep learning speech enhancement and facial expression recognition. Moreover, this study uses emotional calculation as the theoretical basis and uses facial expression recognition as the core technology to judge and understand the emotional state by capturing and recognizing the facial expressions of online learners. In addition, this study has carried out experimental tests on the effect of the identification method of this paper and verified that the method has good detection effect on the real smile micro-expressions through two sets of experiments and can provide theoretical reference for subsequent related research.
机译：在多媒体英语教学中，学习者在没有情感的情况下面临这种无动于衷的电脑屏幕，感受互动和情感刺激的乐趣，这将导致怨恨并影响学习者的学习效果。为了提高多媒体英语教学的效率，旨在缺乏多媒体英语教育的情感，本研究提出了一种基于深度学习言论增强和面部表情识别的智能网络教学系统模型。此外，本研究使用情绪计算作为理论基础，并使用面部表情识别作为核心技术来判断和理解情绪状态，通过捕获和认识在线学习者的面部表情。此外，该研究对本文鉴定方法的影响进行了实验测试，并验证了通过两组实验对真实笑容微观表达的方法具有良好的检测效果，可以为后续相关研究提供理论参考。

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