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Student’s Feedback by emotion and speech recognition through Deep Learning

机译:学生通过深入学习的情感和语音识别的反馈

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Student’s learning is very important part and definitely it is require that student’s attentiveness during learning is very much required. This is possible only when a teacher teaching in the class identifies. But, Time changed many things; COVID19 takes an educational system to a new turn. During online class interaction of teachers and students has became very weak in comparison with face to face learning. The only way to understand student’s attention in class is by their speech and facial expressions emotion’s recognition. Success may be achieved through driving the learner’s attention during online class. In this paper, an innovative approach for video/ image analysis for real time facial expressions is developed, so as to recognize the quality of understanding and active involvement of the attendees in learning and teaching process. This study will help to improve the quality of services in various teaching, trainings, mentoring and consultation which improve and act as a solution to normal feedback procedure of pen and paper method. This system will help both teachers and learners to improve their way of teaching and learning through calculated Concentration Index.
机译:学生的学习是非常重要的部分,绝对需要学生在学习期间的注意力非常需要。只有在课堂上的教师教学识别时,这是可能的。但是,时间改变了很多事情;冠状病毒病 19 将教育系统推向新转。在网上课程期间,与面对面学习相比,教师的互动变得非常弱。了解课堂上学生注意的唯一途径是他们的言语和面部表情情感的认可。通过在在线课程中推动学习者的注意,可以实现成功。在本文中,开发了一种创新的实时面部表情的视频/图像分析方法,以识别与会者在学习和教学过程中的理解和积极参与的质量。本研究将有助于提高各种教学,培训,指导和咨询的服务质量,这些咨询改善和充当笔和纸法正常反馈过程的解决方案。该系统将帮助教师和学习者通过计算的浓度指数来提高他们的教学和学习方式。

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