Yingnan Zhang
ABSTRACT
In recent decades, personality as a long term paralinguistic information has attracted more and more researchers. The main idea of the personality refers to the characteristics which acts as interactions between persons and the social occasions This paper proposes an approach for the automatic prediction of the Big-Five personality traits and 30 sub dimensions the listeners attribute to a speaker they don't know. The experiments are performed over a corpus of 1031 speech clips (337 identities in total) annotated not only Big-Five personality traits, but also all 30 sub-dimensions by using The Revised NEO Personality Inventory (NEO PI-R). The results show that it is possible to predict some particular sub-dimension with high accuracy (more than 75%) whether a person is perceived to be in the higher or lower part of the scales corresponding to each of the 30 sub dimensions, these sub dimensions give personality more accurate descriptions to lay the foundation for a more diversified personality classification
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