Analysis of feature extraction techniques for improved emotion recognition in presence of additive noise

Recently, studies have been performed on identification and classification of feature extraction for emotion recognition. Recognition rate of Speech Emotion Recognition system (SER) degrades when there exist a noisy environment. This paper suggests a new approach of feature extraction for robust emotion recognition in noisy environment. It demonstrates the use of cochlear filterbank with zero-crossing for frequency estimation and Multiclass Support Vector Machine for classification. Experimental results shows that proposed cochlear feature with Zero Crossing (ZC) gives better accuracy in identifying emotional state in voiced signal compared to baseline approach MFCC. When cochlear filterbank coefficients is combined with prosodic feature (i.e. energy and pitch), recognition rate was found to be improved for the same database by 9.92, 2.5, 3.52 (%) for various noise levels in testing dataset.