Automatic Personality Perception Using Autoencoder And Hierarchical Fuzzy Classification

In this research, a study of automatic personality perception based on the Big-five Inventory (BFI) is done. To extract and select appropriate features for the classification, we employ an auto-encoder as a nonlinear feature learning technique. Since an auto-encoder does not extract proper classification lonely, a saddle point is found by a stop criterion based on maximum separate ability in binary classes. The results reveal that nonlinear features enhance the classification results in most personality traits. Furthermore, we use an adaptive neuro-fuzzy inference system classification to model the uncertainty rooted in mental states and affect the classification results through the extracted features. The classification outcomes on SSPNet Speaker Personality dataset demonstrate significant improvement in the results of four traits. These outgrowths verify the existence of uncertainty in the speech signal.