Vyaktitv: A Multimodal Peer-to-Peer Hindi Conversations based Dataset for Personality Assessment

TL;DR

Vyaktitv is a new multimodal dataset of Hindi peer-to-peer conversations with audio, video, and socio-demographic data, designed to advance personality analysis in low-resource languages and social contexts.

Contribution

The paper introduces Vyaktitv, a novel multimodal Hindi conversation dataset with socio-demographic features, enabling research on personality detection in low-resource language social interactions.

Findings

Dataset includes high-quality audio and video recordings.

Contains socio-demographic features like income and cultural orientation.

Provides a foundation for future personality analysis research.

Abstract

Automatically detecting personality traits can aid several applications, such as mental health recognition and human resource management. Most datasets introduced for personality detection so far have analyzed these traits for each individual in isolation. However, personality is intimately linked to our social behavior. Furthermore, surprisingly little research has focused on personality analysis using low resource languages. To this end, we present a novel peer-to-peer Hindi conversation dataset- Vyaktitv. It consists of high-quality audio and video recordings of the participants, with Hinglish textual transcriptions for each conversation. The dataset also contains a rich set of socio-demographic features, like income, cultural orientation, amongst several others, for all the participants. We release the dataset for public use, as well as perform preliminary statistical analysis along the different dimensions. Finally, we also discuss various other applications and tasks for which the dataset can be employed.