Feature extraction of speech signals in emotion identification

In this work, the acoustic and spectral characteristics and the automatic recognition of human emotional states through speech analysis have been studied. Acoustic features have been evaluated and features from time-frequency representation are proposed. The method is based in the representation of speech signal through energy distributions (Gabor transform and WVD) and discrete coefficients (DWT and linear prediction analysis). Recognition accuracy of 94.6% for emotion detection are obtained from SES database of emotional speech in spanish language.