An Emotion Detection System Based on Multi Least Squares Twin Support Vector Machine

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 novelmulti 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.

[1]  Divya Tomar,et al.  A Survey on Pre-processing and Post-processing Techniques in Data Mining , 2014 .

[2]  Madan Gopal,et al.  Least squares twin support vector machines for pattern classification , 2009, Expert Syst. Appl..

[3]  Fakhri Karray,et al.  Survey on speech emotion recognition: Features, classification schemes, and databases , 2011, Pattern Recognit..

[4]  Shashidhar G. Koolagudi,et al.  Emotion recognition from speech: a review , 2012, International Journal of Speech Technology.

[5]  Divya Tomar,et al.  Twin Support Vector Machine: A review from 2007 to 2014 , 2015 .

[6]  Kakuichi Shiomi.  Voice processing technique for human cerebral activity measurement , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[7]  Charles L. Raison,et al.  Inflammation and Its Discontents: The Role of Cytokines in the Pathophysiology of Major Depression , 2009, Biological Psychiatry.

[8]  S. Tokuno,et al.  Usage of emotion recognition in military health care , 2011, 2011 Defense Science Research Conference and Expo (DSR).

[9]  Juanying Xie,et al.  Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases , 2011, Expert Syst. Appl..

[10]  Divya Tomar,et al.  Feature Selection based Least Square Twin Support Vector Machine for Diagnosis of Heart Disease , 2014, BSBT 2014.

[11]  Divya Tomar,et al.  A Feature Selection Based Model for Software Defect Prediction , 2014 .

[12]  Halis Altun,et al.  Boosting selection of speech related features to improve performance of multi-class SVMs in emotion detection , 2009, Expert Syst. Appl..

[13]  Mansour Sheikhan,et al.  Modular neural-SVM scheme for speech emotion recognition using ANOVA feature selection method , 2013, Neural Computing and Applications.

[14]  Ibrahiem M. M. El Emary,et al.  Speech emotion recognition approaches in human computer interaction , 2013, Telecommun. Syst..

[15]  Theodoros Iliou,et al.  Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011 , 2012, Artificial Intelligence Review.

[16]  Divya Tomar,et al.  A survey on Data Mining approaches for Healthcare , 2013, BSBT 2013.

[17]  Tomas Pfister,et al.  Emotion Detection from Speech , 2007 .

[18]  Shuzhi Sam Ge,et al.  Speaker state classification based on fusion of asymmetric simple partial least squares (SIMPLS) and support vector machines , 2014, Comput. Speech Lang..

[19]  Yasue Mitsukura,et al.  Emotional speech classification with prosodic prameters by using neural networks , 2001, The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001.