Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset

TL;DR

This paper presents UDIVA, a comprehensive dataset of face-to-face dyadic interactions with multimodal data, and proposes a transformer-based approach for inferring personality traits using contextual audiovisual information.

Contribution

The paper introduces UDIVA, a novel dataset for dyadic interactions, and develops a transformer-based method for personality inference leveraging multimodal and contextual data.

Findings

Using all context improves personality inference accuracy.

UDIVA dataset includes diverse interaction scenarios and multimodal recordings.

Preliminary results show consistent performance gains with contextual information.

Abstract

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.