Fusion of acoustic, linguistic and psycholinguistic features for Speaker Personality Traits recognition

Behavioral analytics is an emerging research area that aims at automatic understanding of human behavior. For the advancement of this research area, we are interested in the problem of learning the personality traits from spoken data. In this study, we investigated the contribution of different types of speech features to the automatic recognition of Speaker Personality Trait (SPT) across diverse speech corpora (broadcast news and spoken conversation). We have extracted acoustic, linguistic, and psycholinguistic features and modeled their combination as input to the classification task. For the classification, we used Sequential Minimal Optimization for Support Vector Machine (SMO) together with Relief feature selection. The present study shows different levels of performance for automatically selected feature sets, and overall improved performance with their combination across diverse corpora.