Deep Neural Network Analysis of Emotional Synchrony and Its Role in Marital Satisfaction

Abstract

The objective of this study was to examine whether deep neural network–derived indicators of emotional synchrony predict marital satisfaction among married couples in the United States. This study employed a cross-sectional, observational design with a correlational–predictive framework. A total of 286 married couples residing in the United States participated in the study. Couples completed a standardized measure of marital satisfaction and engaged in a structured dyadic interaction task during which facial expressions and vocal signals were recorded. Multimodal emotional data were extracted from synchronized facial and vocal streams and transformed into dynamic emotional synchrony indices capturing both concurrent and lagged emotional alignment between partners. These indices were used as inputs to deep neural network models specifically designed to analyze dyadic temporal data. Model training, validation, and testing were conducted at the couple level to prevent data leakage, and explainable artificial intelligence techniques were applied to identify the most influential emotional features contributing to prediction accuracy. Deep neural network models demonstrated that overall emotional synchrony significantly predicted marital satisfaction, with multimodal models outperforming unimodal models. Models incorporating both facial and vocal synchrony explained a substantially greater proportion of variance in marital satisfaction than models based on static or single-modality features. Positive affect synchrony emerged as the strongest predictor, while negative affect synchrony showed a significant inverse association with marital satisfaction. Temporal models capturing dynamic emotional alignment significantly outperformed static models, indicating the critical role of time-dependent emotional processes in marital relationships. The findings provide compelling evidence that emotional synchrony, particularly dynamic coordination of positive emotions, is a robust predictor of marital satisfaction and highlight the value of deep neural network approaches for advancing the study of emotional processes in intimate relationships.