Deception Detection in Dyadic Exchanges Using Multimodal Machine Learning: A Study on a Swedish Cohort

• URL: http://arxiv.org/abs/2506.21429v1
• Date: Thu, 26 Jun 2025 16:11:42 GMT
• Title: Deception Detection in Dyadic Exchanges Using Multimodal Machine Learning: A Study on a Swedish Cohort
• Authors: Franco Rugolon, Thomas Jack Samuels, Stephan Hau, Lennart Högman,
• Abstract summary: This study investigates the efficacy of using multimodal machine learning techniques to detect deception in dyadic interactions.<n>We compare early and late fusion approaches, utilizing audio and video data - specifically, Action Units and gaze information.<n>The results demonstrate that incorporating both speech and facial information yields superior performance compared to single-modality approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study investigates the efficacy of using multimodal machine learning techniques to detect deception in dyadic interactions, focusing on the integration of data from both the deceiver and the deceived. We compare early and late fusion approaches, utilizing audio and video data - specifically, Action Units and gaze information - across all possible combinations of modalities and participants. Our dataset, newly collected from Swedish native speakers engaged in truth or lie scenarios on emotionally relevant topics, serves as the basis for our analysis. The results demonstrate that incorporating both speech and facial information yields superior performance compared to single-modality approaches. Moreover, including data from both participants significantly enhances deception detection accuracy, with the best performance (71%) achieved using a late fusion strategy applied to both modalities and participants. These findings align with psychological theories suggesting differential control of facial and vocal expressions during initial interactions. As the first study of its kind on a Scandinavian cohort, this research lays the groundwork for future investigations into dyadic interactions, particularly within psychotherapy settings.

Related papers

• Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversation [70.52558242336988]
  We focus on predicting engagement in dyadic interactions by scrutinizing verbal and non-verbal cues, aiming to detect signs of disinterest or confusion. In this work, we collect a dataset featuring 34 participants engaged in casual dyadic conversations, each providing self-reported engagement ratings at the end of each conversation. We introduce a novel fusion strategy using Large Language Models (LLMs) to integrate multiple behavior modalities into a multimodal transcript''
  arXiv  Detail & Related papers  (2024-09-13T18:28:12Z)
• A Study on Bias Detection and Classification in Natural Language Processing [2.908482270923597]
  The aim of our work is to determine how to better combine publicly-available datasets to train models in the task of hate speech detection and classification. We discuss these issues in tandem with the development of our experiments, in which we show that the combinations of different datasets greatly impact the models' performance.
  arXiv  Detail & Related papers  (2024-08-14T11:49:24Z)
• Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection [57.13665112065285]
  Human-Object Interaction (HOI) detection is a challenging computer vision task. We present a framework that enhances HOI detection by incorporating structured text knowledge.
  arXiv  Detail & Related papers  (2023-07-25T14:20:52Z)
• Multimodal Learning Without Labeled Multimodal Data: Guarantees and Applications [90.6849884683226]
  We study the challenge of interaction quantification in a semi-supervised setting with only labeled unimodal data. Using a precise information-theoretic definition of interactions, our key contribution is the derivation of lower and upper bounds. We show how these theoretical results can be used to estimate multimodal model performance, guide data collection, and select appropriate multimodal models for various tasks.
  arXiv  Detail & Related papers  (2023-06-07T15:44:53Z)
• Expanding the Role of Affective Phenomena in Multimodal Interaction Research [57.069159905961214]
  We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing. We identify 910 affect-related papers and present our analysis of the role of affective phenomena in these papers. We find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states.
  arXiv  Detail & Related papers  (2023-05-18T09:08:39Z)
• 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)
• Towards Understanding Confusion and Affective States Under Communication Failures in Voice-Based Human-Machine Interaction [8.602681427083553]
  We present a series of two studies conducted to understand user's affective states during voice-based human-machine interactions. The studies consist of two types of tasks: (1) related to communication with a voice-based virtual agent: speaking to the machine and understanding what the machine says, (2) non-communication related, problem-solving tasks where the participants solve puzzles and riddles but are asked to verbally explain the answers to the machine.
  arXiv  Detail & Related papers  (2022-07-15T18:32:48Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Video Sentiment Analysis with Bimodal Information-augmented Multi-Head Attention [7.997124140597719]
  This study focuses on the sentiment analysis of videos containing time series data of multiple modalities. The key problem is how to fuse these heterogeneous data. Based on bimodal interaction, more important bimodal features are assigned larger weights.
  arXiv  Detail & Related papers  (2021-03-03T12:30:11Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)

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.