Real-TurnTurk: A Multimodal Turkish Corpus for Turn-Taking Prediction

Abstract:Turn-taking is a basic organizational feature of human conversation and remains difficult to model in natural, synchronous dialog systems. While existing research has explored multimodal approaches and large language models for turn-ending prediction, there is a lack of naturalistic conversational corpora specifically addressing turn-taking dynamics in Turkish. This study introduces a multimodal Turkish conversational dataset of unscripted dyadic interactions, comprising synchronized front-facing video, per-speaker audio channels that allow overlapping speech to be attributed to individual speakers, and time-aligned transcriptions. Turn-taking prediction is formulated as a binary classification problem, and a Genetic Algorithm (GA) is employed to optimize interpretable decision rules derived from visual, acoustic, and linguistic features. A hybrid AND-OR rule representation is adopted in the proposed framework to represent the alternative cue combinations that precede a turn transition.

Accepted to INTCEC 2026. This is the author's pre-print version. The final authenticated version will be available through the conference proceedings

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.22071 [cs.CL]

(or arXiv:2608.22071v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.22071 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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