| Paper: | SP-P7.11 | ||
| Session: | Topics in Speech Analysis | ||
| Time: | Wednesday, May 19, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Speech Processing: Speech Analysis | ||
| Title: | AUTOMATIC EMOTIONAL SPEECH CLASSIFICATION | ||
| Authors: | Dimitrios Ververidis; Aristotle University of Thessaloniki | ||
| Constantine Kotropoulos; Aristotle University of Thessaloniki | |||
| Ioannis Pitas; Aristotle University of Thessaloniki | |||
| Abstract: | Our purpose is to design a useful tool which can be used in psychology to automatically classify utterances in emotionalstates such as, anger, happiness, neutral, sadness and surprise. The major contribution of the paper is to rate the discriminating capability 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 forward selection method (SFS) has been used in order to discover the 5-10 features, which are able to classify the utterances in the best way. The criterion used in SFS is the crossvalidated correct classification score of one of the following classifiers: Nearest mean, Parzen windows, and Bayes. After selecting the 5 best features, we reduce the dimensionality to two by applying principal component analysis. The result is a 51.6% +/- 3% (95% conf. int.) correct classification rate 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 apossible reduction of the error rates reported in this paper would be achieved by employing two-class classifiers and combining them. | ||
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