  This paper proposes an approach to detect emotion from human speech employing
majority voting technique over several machine learning techniques. The
contribution of this work is in two folds: firstly it selects those features of
speech which is most promising for classification and secondly it uses the
majority voting technique that selects the exact class of emotion. Here,
majority voting technique has been applied over Neural Network (NN), Decision
Tree (DT), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Input
vector of NN, DT, SVM and KNN consists of various acoustic and prosodic
features like Pitch, Mel-Frequency Cepstral coefficients etc. From speech
signal many feature have been extracted and only promising features have been
selected. To consider a feature as promising, Fast Correlation based feature
selection (FCBF) and Fisher score algorithms have been used and only those
features are selected which are highly ranked by both of them. The proposed
approach has been tested on Berlin dataset of emotional speech [3] and
Electromagnetic Articulography (EMA) dataset [4]. The experimental result shows
that majority voting technique attains better accuracy over individual machine
learning techniques. The employment of the proposed approach can effectively
recognize the emotion of human beings in case of social robot, intelligent chat
client, call-center of a company etc.
