Instance-Based Learning Algorithms
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Machine Learning
How to find trouble in communication
Speech Communication - Special issue on speech and emotion
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Multi-stage classification of emotional speech motivated by a dimensional emotion model
Multimedia Tools and Applications
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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* 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.