Emotion Detection Using MFCC and Cepstrum Features

A tremendous research is being done on Speech Emotion Recognition (SER) in the recent years with its main motto to improve human machine interaction. In this work, the effect of cepstral coefficients in the detection of emotions is performed. Also, a comparative analysis of cepstum, Mel-frequency Cepstral Coefficients (MFCC) and synthetically enlarged MFCC coefficients on emotion classification is done. Using a compact feature vector, our algorithm depicted better recognition rates of identifying seven emotions from Berlin speech corpus compared to the earlier work by Firoz Shah where only four emotions were recognized with good accuracy. The proposed method has facilitated a considerable reduction in the misclassification efficiency which outperforms the algorithm by InmaMohino, where the feature vector included only synthetically enlarged MFCC coefficients.