Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset
This dataset provides a new resource for researchers studying context-aware personality inference in dyadic interactions, addressing the need for more realistic and comprehensive data.
This paper introduces UDIVA, a new dataset of 90.5 hours of face-to-face dyadic interactions among 147 participants, recorded with multiple sensors and including personality and internal state profiling. The authors propose a transformer-based method for self-reported personality inference in dyadic scenarios, showing consistent improvements when using all available context information.
This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self- and peer-reported personality, internal state, and relationship profiling from participants. As an initial analysis on UDIVA, we propose a transformer-based method for self-reported personality inference in dyadic scenarios, which uses audiovisual data and different sources of context from both interlocutors to regress a target person's personality traits. Preliminary results from an incremental study show consistent improvements when using all available context information.