Assessment of Personality Dimensions Across Situations Using Conversational Speech

• URL: http://arxiv.org/abs/2507.19137v1
• Date: Fri, 25 Jul 2025 10:18:28 GMT
• Title: Assessment of Personality Dimensions Across Situations Using Conversational Speech
• Authors: Alice Zhang, Skanda Muralidhar, Daniel Gatica-Perez, Mathew Magimai-Doss,
• Abstract summary: We investigate the relationship between conversational speech and perceived personality for participants engaged in two work situations.<n>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.
• Score: 16.7073312097924
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues [63.936654900356004]
  Personality recognition aims to identify the personality traits implied in user data such as dialogues and social media posts. We propose a novel task named Explainable Personality Recognition, aiming to reveal the reasoning process as supporting evidence of the personality trait.
  arXiv  Detail & Related papers  (2024-09-29T14:41:43Z)
• EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection [19.98674724777821]
  We propose a new personality detection method called EERPD. This method introduces the use of emotion regulation, a psychological concept highly correlated with personality, for personality prediction. Experimental results demonstrate that EERPD significantly enhances the accuracy and robustness of personality detection.
  arXiv  Detail & Related papers  (2024-06-23T11:18:55Z)
• Large Language Models Can Infer Personality from Free-Form User Interactions [0.0]
  GPT-4 can infer personality with moderate accuracy, outperforming previous approaches. Results show that the direct focus on personality assessment did not result in a less positive user experience. Preliminary analyses suggest that the accuracy of personality inferences varies only marginally across different socio-demographic subgroups.
  arXiv  Detail & Related papers  (2024-05-19T20:33:36Z)
• Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation [30.820334868031537]
  Personality Recognition in Conversation (PRC) aims to identify the personality traits of speakers through textual dialogue content. We propose Affective Natural Language Inference (Affective-NLI) for accurate and interpretable PRC.
  arXiv  Detail & Related papers  (2024-04-03T09:14:24Z)
• PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection [50.66968526809069]
  We propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner. Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection.
  arXiv  Detail & Related papers  (2023-10-31T08:23:33Z)
• Identifying and Manipulating the Personality Traits of Language Models [9.213700601337383]
  We investigate whether perceived personality in language models is exhibited consistently in their language generation. We show that language models such as BERT and GPT2 can consistently identify and reflect personality markers in different contexts. This behavior illustrates an ability to be manipulated in a highly predictable way, and frames them as tools for identifying personality traits and controlling personas in applications such as dialog systems.
  arXiv  Detail & Related papers  (2022-12-20T14:24:11Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition [17.428626029689653]
  We propose to represent the target subjects person-specific cognition in the form of a person-specific CNN architecture. Each person-specific CNN is explored by the Neural Architecture Search (NAS) and a novel adaptive loss function. Experimental results show that the produced graph representations are well associated with target subjects' personality traits.
  arXiv  Detail & Related papers  (2021-10-26T11:04:23Z)
• Perception Point: Identifying Critical Learning Periods in Speech for Bilingual Networks [58.24134321728942]
  We compare and identify cognitive aspects on deep neural-based visual lip-reading models. We observe a strong correlation between these theories in cognitive psychology and our unique modeling.
  arXiv  Detail & Related papers  (2021-10-13T05:30:50Z)
• Vyaktitv: A Multimodal Peer-to-Peer Hindi Conversations based Dataset for Personality Assessment [50.15466026089435]
  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.
  arXiv  Detail & Related papers  (2020-08-31T17:44:28Z)
• Towards Persona-Based Empathetic Conversational Models [58.65492299237112]
  Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy. We propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding.
  arXiv  Detail & Related papers  (2020-04-26T08:51:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.