Automatically assessing acoustic manifestations of personality in speech

In this paper, we present first results on applying a personality assessment paradigm to speech input, and comparing human and automatic performance on this task. We cue a professional speaker to produce speech using different personality profiles and encode the resulting vocal personality impressions in terms of the Big Five NEO-FFI personality traits. We then have human raters, who do not know the speaker, estimate the five factors. We analyze the recordings using signal-based acoustic and prosodic methods and observe high consistency between the acted personalities, the raters' assessments, and initial automatic classification results. This presents a first step towards being able to handle personality traits in speech, which we envision will be used in future voice-based communication between humans and machines.