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We propose a multimodal deep learning framework for automated analysis ofcat–human communication, integrating acoustic, visual, and tactile signals throughtransformer-based fusion. Using the largest expert-annotated dataset of its kindand interdisciplinary collaboration, we combine semi-supervised learning withethological and phonetic expertise to detect subtle behavioural and phonetic cues,enable
