文章基本信息

标题：An Emotion Detection System Based on Multi Least Squares Twin Support Vector Machine
本地全文：下载
作者：Divya Tomar ; Divya Ojha ; Sonali Agarwal 等
期刊名称：Advances in Artificial Intelligence
印刷版ISSN：1687-7470
电子版ISSN：1687-7489
出版年度：2014
卷号：2014
DOI：10.1155/2014/282659
出版社：Hindawi Publishing Corporation
摘要：Posttraumatic stress disorder (PTSD), bipolar manic disorder (BMD), obsessive compulsive disorder (OCD), depression, and suicide are some major problems existing in civilian and military life. The change in emotion is responsible for such type of diseases. So, it is essential to develop a robust and reliable emotion detection system which is suitable for real world applications. Apart from healthcare, importance of automatically recognizing emotions from human speech has grown with the increasing role of spoken language interfaces in human-computer interaction applications. Detection of emotion in speech can be applied in a variety of situations to allocate limited human resources to clients with the highest levels of distress or need, such as in automated call centers or in a nursing home. In this paper, we used a novel multi least squares twin support vector machine classifier in order to detect seven different emotions such as anger, happiness, sadness, anxiety, disgust, panic, and neutral emotions. The experimental result indicates better performance of the proposed technique over other existing approaches. The result suggests that the proposed emotion detection system may be used for screening of mental status.