I have conducted one of the first large-scale data mining experiment for the task of recognizing basic emotions in unformal everyday short utterances. This work was focused on the speaker dependant problem. I compared a large set of machine learning algorithms, ranging from neural networks, Support Vector Machines or decision trees, together with 200 features, using a large database of several thousands examples. The difference of performance among learning schemes can be substantial, and some features which were precedendtly unexplored are of crucial importance. An optimal feature set was derived through the use of a genetic algorithm.

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Oudeyer P-Y. (2003) The production and recognition of emotions in speech: features and algorithms, International Journal in Human-Computer Studies, vol. 59/1-2, pp. 157-183, special issue on Affective Computing.  bibtex reference  abstract