Classification of emotions from speech signal
The article presents an analysis of the possibility of recognizing speaker's emotions from speech signal in Polish language. In order to perform experiments a database containing speech recordings with emotional content was created. On its basis, extraction of features from the speech signals was performed. The most important step was to determine which of the previously extracted features were the most suitable to distinguish emotions and with what accuracy the emotions could be classified. Two feature selection methods Sequential Forward Search (SFS) and t statistics were examined. Emotion classification was implemented using k Nearest Neighbor (k NN) Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) classifiers. Classification was carried out for pairs of emotions. The best results were obtained for classifying neutral and fear (91.9% and neutral and joy emotions (89.6%
2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)