计算机科学
情绪识别
利用
领域(数学)
运动捕捉
人工智能
情态动词
深度学习
语音识别
机器学习
运动(物理)
化学
计算机安全
数学
高分子化学
纯数学
作者
Samarth Tripathi,Sarthak Tripathi,Homayoon Beigi
出处
期刊:Cornell University - arXiv
日期:2018-04-16
被引量:4
标识
DOI:10.48550/arxiv.1804.05788
摘要
Emotion recognition has become an important field of research in Human Computer Interactions as we improve upon the techniques for modelling the various aspects of behaviour. With the advancement of technology our understanding of emotions are advancing, there is a growing need for automatic emotion recognition systems. One of the directions the research is heading is the use of Neural Networks which are adept at estimating complex functions that depend on a large number and diverse source of input data. In this paper we attempt to exploit this effectiveness of Neural networks to enable us to perform multimodal Emotion recognition on IEMOCAP dataset using data from Speech, Text, and Motion capture data from face expressions, rotation and hand movements. Prior research has concentrated on Emotion detection from Speech on the IEMOCAP dataset, but our approach is the first that uses the multiple modes of data offered by IEMOCAP for a more robust and accurate emotion detection.
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