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arXiv · 2506.13971

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience

Andrew Chang·Chenkai Hu·Ji Qi·Zhuojian Wei·Kexin Zhang·Viswadruth Akkaraju·David Poeppel·Dustin Freeman
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Abstract

Group conversations over videoconferencing are a complex social behavior. However, the subjective moments of negative experience, where the conversation loses fluidity or enjoyment remain understudied. These moments are infrequent in naturalistic data, and thus training a supervised learning (SL) model requires costly manual data annotation. We applied semi-supervised learning (SSL) to leverage targeted labeled and unlabeled clips for training multimodal (audio, facial, text) deep features to predict non-fluid or unenjoyable moments in holdout videoconference sessions. The modality-fused co-training SSL achieved an ROC-AUC of 0.9 and an F1 score of 0.6, outperforming SL models by up to 4% with the same amount of labeled data. Remarkably, the best SSL model with just 8% labeled data matched 96% of the SL model's full-data performance. This shows an annotation-efficient framework for modeling videoconference experience.

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Andrew Chang, Chenkai Hu, Ji Qi, Zhuojian Wei, Kexin Zhang, Viswadruth Akkaraju, David Poeppel, Dustin Freeman. 2025-06-01. Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience. https://doi.org/10.21437/interspeech.2025-2451

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