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AI models predict conversation turn-taking using gaze and speech

By PulseAugur Editorial · [1 sources] ·

Researchers have developed models to predict turn-taking in multi-party conversations by analyzing speech and gaze dynamics, alongside perceived interpersonal closeness. Using the GaMMA corpus, logistic regression models were trained on features extracted before turn-taking events to classify outcomes as gaps or overlaps. The study found that gaze features, such as transition motifs and mutual gaze, combined with speech features like speaker loudness, significantly improved prediction accuracy (ROC AUC = 0.76). Gaze proved to be a robust, noise-resilient cue for turn-taking, complementing loudness which indicated speaker control. AI

IMPACT This research could lead to more natural human-computer interaction and improved AI agents capable of participating in complex conversations.

RANK_REASON This is a research paper detailing a new modeling approach for conversational dynamics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI models predict conversation turn-taking using gaze and speech

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This is a research paper detailing a new modeling approach for conversational dynamics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Mark Dourado, Karim Haddad, Henrik G. Hassager, Stefania Serafin ·

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

    arXiv:2608.27988v1 Announce Type: new Abstract: 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-…

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