Modeling Speaker Personality using Voice

In this paper, we validate the application of an established personality assessment and modeling paradigm to speech input, and extend earlier work towards text independent speech input. We show that human labelers can consistently label acted speech data generated across multiple recording sessions, and investigate further which of the 5 scales in the NEO-FFI scheme can be assessed from speech, and how a manipulation of one scale influences the perception of another. Finally, we present a clustering of human labels of perceived personality traits, which will be useful in future experiments on automatic classification and generation of personality traits from speech.