Inferring the Human Emotional State of Mind using Assymetric Distrubution

This present paper highlights a methodology for Emotion Recognition based on Skew Symmetric Gaussian Mixture Model classifier and MFCC-SDC ceptral coefficients as the features for the recognition of various emotions from the generated data-set of emotional voices belonging to students of both genders in GITAM University. For training and testing of the developed methodology, the data collection is carried out from the students of GITAM University of Visakhapatnam campus using acting sequence consisting of five different emotions namely Happy, Sad, Angry, Neutral, Boredom; each uttering one short emotional base sentence. For training the data we have considered fifty speakers from different regions (30 male & 20 female) and one long sentence containing an emotional speech from each speaker. The experimentation is conducted on text dependent speech emotion recognition and results obtained are tabulated by constructing a Confusion Matrix and comparing with existing methodology like Gaussian mixture model.

[1]  Y. Srinivas,et al.  An Emotion Recognition System based on Right Truncated Gaussian Mixture Model , 2012 .

[2]  Yixiong Pan,et al.  SPEECH EMOTION RECOGNITION USING SUPPORT VECTOR MACHINE , 2010 .

[3]  Lijiang Chen,et al.  Multi-level Speech Emotion Recognition Based on HMM and ANN , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[4]  Kai-Tai Song,et al.  A new information fusion method for SVM-based robotic audio-visual emotion recognition , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[5]  Qiong Duan,et al.  Speech Emotion Recognition Using Gaussian Mixture Model , 2012 .

[6]  K Suri Babu,et al.  Text-Independent Speaker Recognition using Emotional Features and Generalized Gamma Distribution , 2012 .

[7]  K. S. Rao,et al.  TEXT INDEPENDENT SPEAKER IDENTIFICATION USING FINITE DOUBLY TRUNCATED GAUSSIAN MIXTURE MODEL , 2010 .

[8]  N. Murali Krishna,et al.  Emotion Recognition using Dynamic Time Warping Technique for Isolated Words , 2011 .

[9]  Xiong Chen,et al.  Automatic Speech Emotion Recognition using Support Vector Machine , 2011, Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology.

[10]  Wei Zhang,et al.  A Hybrid GMM and Codebook Mapping Method for Spectral Conversion , 2005, ACII.

[11]  A. Routray,et al.  Emotion recognition from Assamese speeches using MFCC features and GMM classifier , 2008, TENCON 2008 - 2008 IEEE Region 10 Conference.

[12]  Srinivas Yarramalle,et al.  Unsupervised medical image segmentation on brain MRI images using Skew Gaussian distribution , 2011, 2011 International Conference on Recent Trends in Information Technology (ICRTIT).

[13]  Björn W. Schuller,et al.  Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[14]  Ya Li,et al.  Utterance independent bimodal emotion recognition in spontaneous communication , 2011, EURASIP J. Adv. Signal Process..

[15]  R. Rao.  Source Feature Based Gender Identification System Using GMM , 2011 .