Assessment of Personality Dimensions Across Situations Using Conversational Speech

TL;DR

This study explores how conversational speech features relate to perceived personality traits across different situations, revealing that context significantly influences personality perception and that acoustic features can predict perceived traits.

Contribution

It introduces an analysis of context-dependent perceived personality traits using acoustic speech features, highlighting the importance of situational factors in automatic personality perception.

Findings

Perceived personalities vary significantly across different interactions.

Acoustic features like loudness and spectral flux predict perceived traits.

Stressful contexts improve neuroticism prediction, aligning with psychological theories.

Abstract

Prior research indicates that users prefer assistive technologies whose personalities align with their own. This has sparked interest in automatic personality perception (APP), which aims to predict an individual's perceived personality traits. Previous studies in APP have treated personalities as static traits, independent of context. However, perceived personalities can vary by context and situation as shown in psychological research. In this study, we investigate the relationship between conversational speech and perceived personality for participants engaged in two work situations (a neutral interview and a stressful client interaction). Our key findings are: 1) perceived personalities differ significantly across interactions, 2) loudness, sound level, and spectral flux features are indicative of perceived extraversion, agreeableness, conscientiousness, and openness in neutral interactions, while neuroticism correlates with these features in stressful contexts, 3) handcrafted acoustic features and non-verbal features outperform speaker embeddings in inference of perceived personality, and 4) stressful interactions are more predictive of neuroticism, aligning with existing psychological research.