Spoken affect classification using neural networks

This paper aims to build an affect recognition system by analysing acoustic speech signals. A database of 391 authentic emotional utterances was collected from 11 speakers. Two emotions, angry and neutral, were considered. Features relating to pitch, energy and rhythm were extracted and used as feature vectors for a neural network. Forward selection was employed to prune redundant and harmful inputs. Initial results show a classification rate of 86.1%.