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[Submitted on 16 Jul 2026] Title:Multimodal Ambivalence and Hesitancy Recognition with Bidirectional Audio--Text Cross-Attention View a PDF of the paper titled Multimodal Ambivalence and Hesitancy Recognition with Bidirectional Audio--Text Cross-Attention, by TingTing Gu and JinCheng Luo and WenQiu Guo View PDF Abstract:Ambivalence and hesitancy (A/H) are subtle behavioural states that can be expressed through both what a person says and how it is spoken. This paper presents an audio--text framework for video-level A/H recognition. Full-video transcripts and speech waveforms are encoded using pre-trained multilingual BERT and wav2vec~2.0, respectively, and projected into a shared representation space. Two stacked bidirectional cross-attention blocks allow linguistic tokens to attend to acoustic frames and acoustic frames to attend to linguistic context without requiring explicit temporal alignment. The resulting sequences are aggregated through masked mean pooling and combined using concatenation, element-wise products, and absolute differences for binary classification. We evaluate the model on the subject-independent split of the Behavioural Ambivalence/Hesitancy (BAH) dataset. The proposed system achieves 72.19\% accuracy, 75.00\% precision, 81.13\% recall, 77.95\% A/H F1-score, and 70.16\% Macro-F1 on the test set. These results demonstrate the potential of jointly modelling linguistic and acoustic information for automatic A/H this http URL at:this https URL. Subjects: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS) Cite as: arXiv:2610.26868 [cs.CL] (or arXiv:2610.26868v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2610.26868 arXiv-issued DOI via DataCite Submission history From: Jincheng L [view email] [v1] Thu, 16 Jul 2026 13:48:35 UTC (364 KB) Full-text links: Access Paper: View a PDF of the paper titled Multimodal Ambivalence and Hesitancy Recognition with Bidirectional Audio--Text Cross-Attention, by TingTing Gu and JinCheng Luo and WenQiu Guo View PDF view license Additional Features Audio Summary Current browse context: cs.CL new | recent | 2026-10 Change to browse by: cs cs.SD eess eess.AS References & Citations SciX Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)