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

DOI: 10.1155/2014/282659

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Abstract:

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. 1. Introduction Stressful situation can cause some major psychiatric problems such as depression, suicide, PTSD, BMD, and OCD in civilian as well as in military life. Earlier treatment may become useful for such type of psychiatric problems [1]. So, there is a need to develop technology for recognizing early change in human behavior. Several biomarkers are reported by the medical researchers for psychiatric diseases [1, 2]. But these biomarkers are not effective in military life as they required a big and complicated machine for detecting psychiatric diseases. On the other hand, there is a fast development in voice, speech, and emotion detection technologies in engineering field. These technologies provide human-machine interaction for emotion detection and further treatment of psychiatric problems [3–5]. Several researches measured the level of fatigue and stress from speech [6]. But the level of fatigue and stress does not lead to psychiatric disorder directly. Emotion change of a human can cause mental diseases. Mostly, clinicians recognize the mental state of a patient from his/her face and voice which represents his/her emotion. This fact leads to the possibility that emotion detection system can be used for recognizing the mental disorder or disease in human. Early detection of disease improves the prognosis and is helpful to provide

References

[1]  S. Tokuno, G. Tsumatori, S. Shono et al., “Usage of emotion recognition in military health care,” in Proceedings of the Defense Science Research Conference and Expo (DSR '11), pp. 1–5, Singapore, August 2011.
[2]  A. H. Miller, V. Maletic, and C. L. Raison, “Inflammation and its discontents: the role of cytokines in the pathophysiology of major depression,” Biological Psychiatry, vol. 65, no. 9, pp. 732–741, 2009.
[3]  C.-N. Anagnostopoulos, T. Iliou, and I. Giannoukos, “Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011,” Artificial Intelligence Review, 2012.
[4]  S. G. Koolagudi and K. S. Rao, “Emotion recognition from speech: a review,” International Journal of Speech Technology, vol. 15, no. 2, pp. 99–117, 2012.
[5]  S. Ramakrishnan and I. M. M. El Emary, “Speech emotion recognition approaches in human computer interaction,” Telecommunication Systems, vol. 52, no. 3, pp. 1467–1478, 2013.
[6]  K. Shiomi, “Voice processing technique for human cerebral activity measurement,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC '08), pp. 3343–3347, October 2008.
[7]  T. Pfister, “Emotion Detection from Speech,” 2010.
[8]  H. Sato, Y. Mitsukura, M. Fukumi, and N. Akamatsu, “Emotional speech classification with prosodic prameters by using neural networks,” in Proceedings of the 7th Australian and New Zealand Intelligent Information Systems Conference, pp. 395–398, IEEE, 2001.
[9]  M. El Ayadi, M. S. Kamel, and F. Karray, “Survey on speech emotion recognition: features, classification schemes, and databases,” Pattern Recognition, vol. 44, no. 3, pp. 572–587, 2011.
[10]  H. Altun and G. Polat, “Boosting selection of speech related features to improve performance of multi-class SVMs in emotion detection,” Expert Systems with Applications, vol. 36, no. 4, pp. 8197–8203, 2009.
[11]  D. Tomar and S. Agarwal, “A survey on data mining approaches for healthcare,” International Journal of Bio-Science and Bio-Technology, vol. 5, no. 5, pp. 241–266, 2013.
[12]  D.-Y. Huang, Z. Zhang, and S. S. Ge, “Speaker state classification based on fusion of asymmetric simple partial least squares (SIMPLS) and support vector machines,” Computer Speech and Language, vol. 28, no. 2, pp. 392–414, 2014.
[13]  M. Sheikhan, M. Bejani, and D. Gharavian, “Modular neural-SVM scheme for speech emotion recognition using ANOVA feature selection method,” Neural Computing and Applications, vol. 23, no. 1, pp. 215–227, 2013.
[14]  M. A. Kumar and M. Gopal, “Least squares twin support vector machines for pattern classification,” Expert Systems with Applications, vol. 36, no. 4, pp. 7535–7543, 2009.
[15]  D. Tomar and S. Agarwal, “Twin support vector machine: a review from 2007 to 2014,” Egyptian Informatics Journal. In press.
[16]  2014, https://archive.ics.uci.edu/ml/datasets.html.
[17]  2014, http://www.fon.hum.uva.nl/praat.
[18]  http://www.stanford.edu/dept/linguistics/corpora/material/PRAAT_workshop_manual_v421.pdf.
[19]  D. Tomar and S. Agarwal, “Feature selection based least square twin support vector machine for diagnosis of heart disease,” International Journal of Bio-Science and Bio-Technology, vol. 6, no. 2, pp. 69–82, 2014.
[20]  S. Agarwal and D. Tomar, “A feature selection based model for software defect prediction,” International Journal of Advanced Science and Technology, vol. 65, pp. 39–58, 2014.
[21]  D. Tomar and S. Agarwal, “A survey on pre-processing and post-processing techniques in data mining,” International Journal of Database Theory & Application, vol. 7, no. 4, 2014.
[22]  J. Xie and C. Wang, “Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases,” Expert Systems with Applications, vol. 38, no. 5, pp. 5809–5815, 2011.

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