Recognition of Emotions from Speech using Excitation Source Features

Abstract In this paper, the speech database is introduced for characterizing the emotions present in speech. A semi natural database GEU-SNESC (GEU Semi Natural Emotion Speech Corpus) is used for obtaining emotion specific information using LP residual samples as features. The corpus is collected by recording dialogues of popular film actors/actresses from Hindi movies. The emotions which are considered in this study are sad, anger, happy and neutral. In this paper Linear Prediction (LP) residual of speech signal is used for characterizing the basic emotions present in the speech. LP residual is obtained by LP analysis, by inverse filtering of the speech signal. For capturing the emotion specific information from the higher order relations, present in the LP residual, Gaussian mixture models (GMM) are used. The emotion recognition performance is observed to be about 50-60%.

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