Automatic emotion recognition using prosodic parameters

This paper presents the experiments made to automatically identify emotion in an emotional speech database for Basque. Three different classifiers have been built: one using spectral features and GMM, other with prosodic features and SVM and the last one with prosodic features and GMM. 86 prosodic features were calculated and then an algorithm to select the most relevant ones was applied. The first classifier gives the best result with a 98.4% accuracy when using 512 mixtures, but the classifier built with the best 6 prosodic features achieves an accuracy of 92.3% in spite of its simplicity, showing that prosodic information is very useful to identify emotions.