Oflazoglu, CaglarYildirim, Serdar2024-09-182024-09-182015978-1-4673-7386-92165-0608https://hdl.handle.net/20.500.12483/815623nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEYEmotion recognition from speech plays important role for natural human-computer interaction. This study investigates binary classification performances of 4 fundamental emotion classes in Turkish Emotional Speech (TurES) Database using acoustic features for various classifiers. Results shows that Angry emotion class has higher classification rate (70%-80%) than others; lowest classification rate is obtained as 64% for Happy-Neutral emotion pair. Best classification results are obtained with J48 (C4.5) classifier for all emotion pairs.trinfo:eu-repo/semantics/closedAccessbinary classificationemotional speechcategorical classificationTurES databasepattern recognitionConference Object235323562-s2.0-84939178773N/AWOS:000380500900570N/A