Assessment of Personality Dimensions Across Situations Using Conversational Speech
This work addresses the need for context-aware personality prediction in assistive technologies, though it is incremental by building on prior APP research with situational variations.
The study tackled the problem of automatic personality perception (APP) by analyzing conversational speech across different situations, finding that perceived personalities vary significantly and that specific acoustic features are more predictive in neutral versus stressful contexts, with handcrafted features outperforming speaker embeddings.
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.