Automatic Personality Perception: Prediction of Trait Attribution Based on Prosodic Features

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Abstract

Whenever we listen to a voice for the first time, we attribute personality traits to the speaker. The process takes place in a few seconds and it is spontaneous and unaware. While the process is not necessarily accurate (attributed traits do not necessarily correspond to the actual traits of the speaker), still it significantly influences our behavior toward others, especially when it comes to social interaction. This paper proposes an approach for the automatic prediction of the traits the listeners attribute to a speaker they never heard before. The experiments are performed over a corpus of 640 speech clips (322 identities in total) annotated in terms of personality traits by 11 assessors. The results show that it is possible to predict with high accuracy (more than 70 percent depending on the particular trait) whether a person is perceived to be in the upper or lower part of the scales corresponding to each of the Big -Five, the personality dimensions known to capture most of the individual differences.

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