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This research introduces CAMA-BC, a framework that integrates visual cues like facial expressions and gestures with audio-text data to significantly improve backchannel prediction accuracy. By employing Multi-Layer Multimodal Alignment, the model effectively captures conversational contexts essential for recognizing complex empathetic responses in human interaction. The study demonstrates clear performance gains over existing unimodal or simple multimodal baselines, advancing natural language understanding through richer sensory inputs.
Open Your Model's Eyes: Video and Context-Aware Multimodal Backchannel Prediction
arXiv:2607.22729v1 Announce Type: cross Abstract: Backchannels, which signal listener states like empathy and understanding, are fundamental to natural human interaction. However, current approaches rely solely on audio and text. This omits crucial visual cues, such as facial expressions and gestures, as well as broader conversational contexts, which are necessary for accurate prediction. In this paper, we introduce Context-Aware Multimodal Alignment for Backchannel Prediction (CAMA-BC), a novel framework that leverages visual information through Multi-Layer Multimodal Alignment (MMA). Our alignment process comprises two stages. First, Context Alignment (MMA-CA) utilizes unlabeled dialogues with videos to capture conversational contexts. Next, Backchannel Alignment (MMA-BA) fine-tunes the representations specifically for backchannel prediction. Experimental results show that CAMA-BC significantly outperforms both existing methods and simple multimodal baselines, with particular effectiveness in recognizing complex backchannels such as empathy.
Abstract:Backchannels, which signal listener states like empathy and understanding, are fundamental to natural human interaction. However, current approaches rely solely on audio and text. This omits crucial visual cues, such as facial expressions and gestures, as well as broader conversational contexts, which are necessary for accurate prediction. In this paper, we introduce Context-Aware Multimodal Alignment for Backchannel Prediction (CAMA-BC), a novel framework that leverages visual information through Multi-Layer Multimodal Alignment (MMA). Our alignment process comprises two stages. First, Context Alignment (MMA-CA) utilizes unlabeled dialogues with videos to capture conversational contexts. Next, Backchannel Alignment (MMA-BA) fine-tunes the representations specifically for backchannel prediction. Experimental results show that CAMA-BC significantly outperforms both existing methods and simple multimodal baselines, with particular effectiveness in recognizing complex backchannels such as empathy.
Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
arXiv:2607.22729 [cs.CV]
(or arXiv:2607.22729v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.22729 arXiv-issued DOI via DataCite (pending registration)
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, pp. 3738-3755, 2026
https://doi.org/10.18653/v1/2026.acl-long.171 DOI(s) linking to related resources
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