Emotion Recognition using combination of MFCC and LPCCwith Supply Vector Machine
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2015, Vol 17, Issue 4
Abstract
Abstract: Speech is a medium through which emotions are expressed by human being. In this paper, a mixtureof MFCC and LPCC has been proposed for audio feature extraction. One of the greatest advantage of MFCC isthat it is capable of identifying features even in the existence of noise and henceforth it is combined with theadvantage of LPCC which helps in extracting features in low acoustics. Two databases have been considerednamely Berlin Emotional Database and a SAVEE database. SVM has been implemented for classification ofseven emotions (Sadness, Joy, Fear, Anger, Boredom, Disgust and Neutral). Accuracy of the developed modelis presented using confusion matrix. The Recognition outcome of the combined MFCC and LPCC extractedfeatures are compared with the isolated results. The maximum accuracy rate that reaches by using the combination of feature extraction method is 88.59% for non linear RBF (Radial Basis Function) kernel SVM.
Authors and Affiliations
Soma Bera , Shanthi Therese , Madhuri Gedam
Keywords
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