Personality computing explores methods of automatically measuring human traits to create a better understanding of the human psyche and thought processes. We examine conversations and interactions in dyadic environments through the perspective of representation learning to capture the psychological traits that compose a target's personality profile. We propose a bimodal speech-text model to predict scores for personality traits at a sentence level for the speakers using disentangled representations on speech and text. Our model outperforms current personality prediction methods using visual features and/or metadata on the UDIVA dataset's English subset.
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