Classification of Emotions from Speech using Implicit Features

Human computer interaction with the time has extended its branches to many different other fields like engineering, cognition, medical etc. Speech analysis has also become an important area of concern. People involved are using this mode for the interaction with the machines to bridge the gap between physical and digital world. Speech emotion recognition has become an integral subfield in the domain. Human beings have an excellent capability to determine the situation by knowing the emotions, and can change the emotion of interaction depending on the context. In the following work the implicit features of the speech have been used for the detection of emotions like anger, happiness, sadness, fear and disgust. As a data set, we have used a standard Berlin emotional database for testing. The classification is done using SVM (support vector machine) which is found to be more consistent with all the emotions as compared to ANN (artificial neural network).