Publication

Abstract : The emotional database can be classified as spontaneous and simulated emotions. Spontaneous emotions can be identified based on the two parameters 1) Arousal and 2) Valence values represented in a two dimensional plane. Arousal measures how calming or exciting the information is, whereas valence measures postive or negative affectivity of information. The objective of the paper is to predict the arousal and valence values from the speech signal which in turn provides the spontaneous emotion information. This paper also provides the significance of using pitch contours obtained from the speech signals having the spontaneous emotion information and is used as features for predicting the arousal and valence values using Deep learning based LSTM models. During testing pitch contours are extracted from the speech signals and used as the features for predicting the arousal and valence values so as to predict the emotions. In this paper a spontaneous database, REmote COLlaborative Affective interaction (RECOLA) database is used. The arousal and valence values predicted in this work has low RMSE. The effectiveness of using pitch to predict 2D values of emotional wheel is also compared on the full blown simulated German emotional database (German EmoDb) and better results were obtained on simulated database also.