Voting ensembles for spoken affect classification

Affect or emotion classification from speech has much to benefit from ensemble classification methods. In this paper we apply a simple voting mechanism to an ensemble of classifiers and attain a modest performance increase compared to the individual classifiers. A natural emotional speech database was compiled from 11 speakers. Listener-judges were used to validate the emotional content of the speech. Thirty-eight prosody-based features correlating characteristics of speech with emotional states were extracted from the data. A classifier ensemble was designed using a multi-layer perceptron, support vector machine, K*K* instance-based learner, K-nearest neighbour, and random forest of decision trees. A simple voting scheme determined the most popular prediction. The accuracy of the ensemble is compared with the accuracies of the individual classifiers.