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Recognition of emotion in speech
with prosodic processing
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.
For more details:
Oudeyer P-Y. (2003) The production and
recognition of emotions in speech: features and algorithms, International Journal in Human-Computer Studies ,
59(1-2), pp. 157--183, special issue on Affective Computing. Bibtex
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