Experimental study in emotion recognition using prosodie features

The paper describes an experimental study on emotion recognition using a collection of emotional recordings from SRoL corpus. Its goal is to study and to obtain a simple tool that can be used in recordings validation in the process of building large voice corpora. The tools can help or even replace the human validation. In this study we used two classifiers, k-NN (k - Nearest Neighborhood) and SVM (Support Vector Machines), and seven different sets of feature vectors based on F0 formant values. After a small introduction, the database and the feature vectors are presented. Commonly used evaluation measures, including Recall, Precision, Accuracy, MCC (Matthews correlation coefficient), computed from the confusion matrix obtained in emotion recognition are then enumerated. Next, there are presented and discussed the achieved results, that can be used in a large variety of possible applications. The presented work is validated by the human validation already done on SRoL corpus.