Our purpose is to design a useful tool which can be used in psy- chology to automatically classify utterances into five emotional states such as anger, happiness, neutral, sadness, and surprise. The major contribution of the paper is to rate the discriminating capa- bility of a set of features for emotional speech recognition. A total of 87 features has been calculated over 500 utterances from the Danish Emotional Speech database. The Sequential Forward Se- lection method (SFS) has been used in order to discover a set of 5 to 10 features which are able to classify the utterances in the best way. The criterion used in SFS is the crossvalidated correct clas- sification score of one of the following classifiers: nearest mean and Bayes classifier where class pdfs are approximated via Parzen windows or modelled as Gaussians. After selecting the 5 best fea- tures, we reduce the dimensionality to two by applying principal component analysis. The result is a 51.6% ± 3% correct classifi- cation rate at 95% confidence interval for the five aforementioned emotions, whereas a random classification would give a correct classification rate of 20%. Furthermore, we find out those two- class emotion recognition problems whose error rates contribute heavily to the average error and we indicate that a possible reduc- tion of the error rates reported in this paper would be achieved by employing two-class classifiers and combining them..
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