Multimodal Ambivalence and Hesitancy Recognition with Bidirectional Audio--Text Cross-Attention

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 recognition.code at:https://github.com/DorriG/ECCV.