Emotion Recognition from Speech using SVM and Random Forest Classifier

Abstract

Speech is the most natural way of people to communicate with one another. It is a vital medium for communicating a person's thoughts, feelings, and mental condition to others. The process of identifying the intellectual state is the recognition of basic emotion through speech. In human life, emotions are incredibly significant. In this project, the emotion is recognized from speech using Support Vector Machine (SVM) and Random Forest classifiers. These are supervised machine learning algorithms used for both classification and regression problems. SVM classifies data by creating N-dimensional hyper planes that divide the input into different categories. The classification is accomplished using a linear and non-linear separation surface in the dataset's input feature. Random Forest is a classifier that combines a number of decision trees on different subsets of a dataset and averages the results to increase the dataset's predicted accuracy. These classifiers are used to categorize emotions like happiness, rage, sadness and neutral for a certain incoming voice signal. Here, the system is trained and developed to recognize emotion in real-time speech. The result demonstrates that the Random Forest classifier is significantly better, when compared to the SVM classifier.