Negation is typically modeled through its linguistic realization, although spoken interaction is accompanied by tightly coordinated nonverbal behavior. We ask whether contexts centered on spoken negation cues contain measurable multimodal behavioral information: whether they can be distinguished from matched control contexts without lexical or acoustic input, where this information occurs in time, which modalities carry it, and whether it extends to the dialogue partner. We study 27 human-human interviews conducted in virtual reality, comprising temporally aligned gaze, facial, head, body, hand, and finger behavior and 964 annotated negation cues. Treating classification as a predictive probe, we compare 20 time-series models while excluding lexical and acoustic information, and then systematically vary temporal context, interactional source, modality availability, and event timing. Across grouped 10-fold cross-validation, the strongest probes reach up to .75 mean held-out AUROC from speaker-side behavior. Temporal analyses show that predictive information is concentrated around cue onset but remains detectable over a broader surrounding interval, while dialogue-partner behavior carries weaker predictive information with a comparatively diffuse temporal profile. Ablation and timing perturbations further show that facial features produce the largest modality-ablation effect and that the trained probe is sensitive to the temporal organization of the observed events.
Multimodal fusion systems for human–robot interaction predominantly rely on snapshot-level features that capture what nonverbal signals are present at a given moment, ignoring when they occur, how long they last, and in what order they unfold. Nevertheless, the temporal dynamics of human behaviour may convey communicat...
Eunyoung Hwang, Alessandra Rossi, Silvia Rossi· Proceedings of the 28th Inte...· 0 citations
The results show that gesture alone is predictive of the intended referent and that multimodal fusion is most beneficial when the transcript-based model is uncertain, and that training-only alignment of learned representations with the referent image further improves the fusion model performance.
E. Ghaleb, Hugh Mee Wong, Kristina Kobrock· 1 citation
A scalable framework that identifies text-biased surface interpretations and converts disagreement regions into conflict QA examples and develops an agentic-style reasoning framework that converts speech into an Audio Twin, a text-readable representation of localized acoustic cues that exposes acoustic evidence to the...
Yen-Ju Lu, Yu-Zhe Wang, Yao-Han Guan et al.· 1 citation
Human communication unfolds as a continuous multimodal stream in which speech, prosody, and gesture are tightly coordinated. Yet gesture is often analyzed as a sequence of discrete, manually segmented events, separated from lexical semantics. This study asks whether subtle continuous wrist movements co-ocurring with sp...
Hear2Act is introduced, a unified evaluation protocol for text and spoken assistants with 480 persona-grounded scenarios, hidden user concerns, and objectively verifiable outcomes that show that prosody matters when lexical evidence is insufficient, and that audio-capable LLMs can recover information from speech but do...
Xin-Yi Liu, H. Nayyeri, Dilek Hakkani-Tur et al.· 3 citations· ⚡1
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