<p>Emotion recognition from speech helps us in improving the effectiveness of human-machine interaction. This paper presents a method to identify suitable features in DWT domain and improve good accuracy. In this work, 7 emotions (Berlin Database) are recognized using Support Vector Machine (SVM) classifier. Entropy of Teager Energy operated Discrete Wavelet Transform (DWT) coefficients, Linear Predictive Cepstral Coefficients(LPCC), Mel Energy Spectral Dynamic Coefficients(MEDC), Zero Crossing Rate (ZCR), shimmer, spectral roll off, spectral flux, spectral centroid, pitch, short time energy and Harmonic to Noise Ratio (HNR) are considered as features. The obtained average accuracy is 82.14 % Earlier work done on emotion recognition using DWT coefficients yielded an accuracy of 63.63 % and 68.5% for 4 emotions on Berlin and Malayalam databases respectively. The proposed algorithm shows a significant increase in accuracy of about 15% to 20% for 7 emotions on Berlin database. Also, 100% efficiency has been achieved for four emotions with Simple Logistic classifier of WEKA 3.6 tool. © 2015 IEEE.</p>
cited By 0; Conference of 6th IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2015 ; Conference Date: 10 December 2015 Through 12 December 2015; Conference Code:120030
S. Lalitha, Mudupu, A., Nandyala, B. V., and Munagala, R., “Speech emotion recognition using DWT”, in 2015 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2015, 2015.