<ul data-eligibleForWebStory="true"><li>This study examines the use of multimodal machine learning to detect deception in dyadic interactions, integrating data from both deceivers and deceived.</li><li>The study compared early and late fusion approaches using audio and video data, specifically focusing on Action Units and gaze information.</li><li>Results show that combining speech and facial data enhances deception detection accuracy, with the best performance (71%) achieved through late fusion across modalities and participants.</li><li>The research on a Swedish cohort suggests that including data from both participants improves detection accuracy and lays the groundwork for future studies in dyadic interactions, especially in psychotherapy settings.</li></ul>

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

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