Emotion recognition using MLP and GMM for Oriya language

Abstract

Emotion recognition of human beings is one of the major challenges in the modern complicated world of political and criminal scenario. In this paper an attempt is taken to recognise two classes of speech emotions as high arousal like angry, surprise and low arousal like sad and bore. Linear prediction coefficients (LPC), Mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) features are used for emotions recognition using multilayer perceptron (MLP) and Gaussian mixture model (GMM) classifier. Two different databases of four emotions, one of five children and other one of a professional actor has been used in this work. Emotion recognition performance of LPC, PLP and MFCC features has been compared with two classifiers, MLP and GMM. MFCC features with MLP classifier and PLP features with GMM classifier has performed best in their respective categories.