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Predicting Turn-Taking Outcomes in Multi-Party Conversation: Interpretable Modelling of Speech and Gaze Dynamics with Interpersonal Closeness — arXiv:2608.27988

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.27988
License (as archived): CC0-1.0
Archived: 2026-10-17T10:56:34.829Z
SHA-256 of the archived text: e3a90f7aa50177ef353c5ad9d67a9543a4a7a12867e587a0697fa0bb2ae8b659
Stable URL of this page: https://metraipolis.com/documento/dc_4906bb0ee9be

How to cite

Predicting Turn-Taking Outcomes in Multi-Party Conversation: Interpretable Modelling of Speech and Gaze Dynamics with Interpersonal Closeness — arXiv:2608.27988 — arXiv (en), https://arxiv.org/abs/2608.27988 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4906bb0ee9be (Metraipolis archive dc_4906bb0ee9be, sha256 e3a90f7aa50177ef353c5ad9d67a9543a4a7a12867e587a0697fa0bb2ae8b659)

BibTeX

@misc{dc4906bb0ee9be,
  title = {Predicting Turn-Taking Outcomes in Multi-Party Conversation: Interpretable Modelling of Speech and Gaze Dynamics with Interpersonal Closeness — arXiv:2608.27988},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.27988},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4906bb0ee9be; sha256 e3a90f7aa50177ef353c5ad9d67a9543a4a7a12867e587a0697fa0bb2ae8b659},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.27988. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Smooth speaker transitions are fundamental to effective conversation and rely on an interlocutor's ability to predict when to enter the conversation. This ability depends on accurately interpreting and expressing the verbal and non-verbal cues that signal when a speaker wishes to take or relinquish the floor. The process becomes even more complex in noisy, natural, multi-party settings, with multiple interlocutors available. This study models how gaze and speech, together with perceived interpersonal closeness, signal conversational floor changes in free four-person dialogue. Using the GaMMA corpus, we trained logistic regression models using interpretable, behaviourally motivated features extracted before each turn-taking event to classify floor-transfer outcomes as gaps or overlaps. Predictors included gaze features such as transition motifs and behavioural contrasts, entropy, gaze-based addressee identity, and mutual gaze, alongside speech features derived from speaker loudness, as well as perceived interpersonal closeness (IOS) between speakers. Results show that gaze features capture predictive structure, and that combining them with loudness improves performance (ROC AUC = 0.76 +- 0.04). Loudness reflected speaker control, while gaze dispersion and addressing indexed listener readiness and competitive entry. Performance remained robust across noise conditions, indicating that gaze provides a complementary, noise-resilient cue to turn-taking dynamics.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-31 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL) ; Sound (cs.SD).
Pagina del lavoro: https://arxiv.org/abs/2608.27988 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T10:47:33.123Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).