Improving emotion recognition from speech using sensor fusion techniques

In this paper, we propose a two level hierarchical ensemble of classifiers for improved recognition of emotion from speech. At the first level, Mel Frequency Cepstral Coefficients (MFCC) of input speech are classified independently by suitably trained Support Vector Machine (SVM) and Gaussian Mixer Model (GMM) classifiers. From these first level classifiers, posterior probabilities of GMM and discriminate function values of SVM are extracted and given as input to second level SVM classifier, which classifies emotion based on these values. Extensive experiments were carried out using the Berlin database Emo-DB for seven emotions (anger, fear, bore, happy, neutral, disgust and sad). While the SVM and GMM classifiers produced only 67% and 66% accuracy respectively, 75% accuracy was achieved with our fusion approach.