Performance Analysis of Malayalam Language Speech Emotion Recognition System Using ANN/SVM. (2016) - Rajisha, T.M.; Sunija, A.P.; Riyas, K.S.
Abstract: Automatic recognition of emotions from speech by machines has been one of the most challenging areas of research in the field of human machine interaction. Automatic emotion recognition system by speech merely means that to monitor and identify the emotional or physiological state of an individual from their utterances. Speech emotion recognition has wide range of application ranging from clinical studies to robotics. In this paper developed speech emotional database for Malayalam language (One of the south Indian languages) and a system for recognizing the emotions. The system used Mel Frequency Cepstral Coefficients (MFCCs), Short Time Energy (STE) and Pitch as features extraction techniques. Two classifiers, namely Artificial Neural Network (ANN) and Support Vector Machine (SVM) used for pattern classification. Experiments show that this method provides a high accuracy of 88.4% in the case of ANN and 78.2% in the case of SVM.