Significance of natural elicitation in developing simulated full blown speech emotion databases

Abstract

The work presented in this paper investigates the significance of natural elicitation of emotions during the development of simulated full blown emotion speech databases emotion analysis. A subset of primary emotions such as anger, happy and sad emotions along with neutral utterances are used in the present work. The first part of the work discusses the development of a simulated full blown emotion database by selecting 50 emotionally biased prompts for the recording the emotional speech data in Tamil language. For the comparative study, another simulated emotion database is developed by recording 50 neutral utterances for recording the emotion speech from the same speakers. The second part of the work is the comparison of emotion recognition performance of the simulated emotion speech databases using the basic Gaussian mixture model (GMM) based system with mel frequency cepstral coefficients (MFCC). A significant variations in the recognition rates of different emotions are observed for both the databases with emotionally biased utterances and emotionally neutral emotion utterances. Where the emotionally biased utterances observed to be more effective in discriminating emotions than emotionally neutral simulated emotion database. Also, the emotion recognition rates obtained for the simulated emotionally neutral emotion utterances follow the same trend as that of the classical German full blown simulated emotion database.