Harmony search for feature selection in speech emotion recognition

Feature selection is a significant aspect of speech emotion recognition system. How to select a small subset out of the thousands of speech data is important for accurate classification of speech emotion. In this paper we investigate heuristic algorithm Harmony search (HS) for feature selection. We extract 3 feature sets, including MFCC, Fourier Parameters (FP), and features extracted with The Munich open Speech and Music Interpretation by Large Space Extraction (openSMILE) toolkit, from Berlin German emotion database (EMODB) and Chinese Elderly emotion database (EESDB). And combine MFCC with FP as the fourth feature set. We use Harmony search to select subsets and decrease the dimension space, and employ 10-fold cross validation in LIBSVM to evaluate the change of accuracy between selected subsets and original sets. Experimental results show that each subset's size reduced by about 50%, however, there is no sharp degeneration on accuracy and the accuracy almost maintains the original ones.