Real-Time Speech Emotion Recognition Using Support Vector Machine

In this paper we present an approach for Real-time emotion recognition from speech using Support Vector Machine (SVM) as a classification technique. Automatic Speech Emotion Recognition (ASER) is an upcoming research area in the field of Human Computer Interaction Intelligence (HCII). Human emotions can be detected from their speech signals by extracting some of the speech acoustic and prosodic features like pitch, Mel frequency Cepstral Coefficient (MFCC)and Mel Energy Spectrum Dynamic Coefficient (MEDC). Here SVM classifier is used to classify the emotions as anger, fear, neutral, sad, disgust, happy and boredom. UGA and LDC datasets are used for offline analysis of emotions using LIBSVM kernel functions.With this analysis the machine is trained and designed for detecting emotions in real time speech.