Use of Different Features for Emotion Recognition Using MLP Network

Emotion recognition of human being is one of the major challenges in 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 and surprise and low arousal like sad and bore. Linear prediction coefficients (LPC), linear prediction cepstral coefficient (LPCC), Mel frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) features are used for emotion recognition using multilayer perception (MLP). Various emotional speech features are extracted from audio channel using above-mentioned features to be used in training and testing. Two hundred utterances from ten subjects were collected based on four emotion categories. One hundred and seventy-five and twenty-five utterances have been considered for training and testing purpose.