Comparative study of multi-stage classification scheme for recognition of Lithuanian speech emotions

This paper presents the experimental study of multi-stage classification based recognition of Lithuanian speech emotions. Three different criteria for feature selection were compared for this purpose: Maximal Efficiency, Minimal Cross-Correlation feature criterions, and the Sequential Feature Selection. A large database of spoken emotional Lithuanian language was used in this experiment - each of 5 emotions was represented by 1000 utterances. The results of the speaker-independent emotion recognition experiment show the superiority of multi-stage classification using the SFS technique by 0.7-8 %. This classification scheme gave the highest recognition accuracy and the smallest feature set.