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

The study investigates how gaze and speech cues, together with perceived interpersonal closeness, predict turn‑taking outcomes in free four‑person conversations. Using the GaMMA corpus, logistic regression models were trained on interpretable features such as gaze transition motifs, entropy, addressee identity, mutual gaze, and speaker loudness to classify floor‑transfer events as gaps or overlaps. Results show that gaze features alone capture predictive structure, and combining them with loudness yields a robust classifier (ROC AUC = 0.76 ± 0.04) that remains effective even under noisy conditions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.