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.