Real-TurnTurk: A Multimodal Turkish Corpus for Turn-Taking Prediction
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Computer Science > Computation and Language
Title: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.
| Comments: | Accepted to INTCEC 2026. This is the author's pre-print version. The final authenticated version will be available through the conference proceedings |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.22071 [cs.CL] |
| (or arXiv:2608.22071v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.22071
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Ahmet Tuğrul Bayrak [view email][v1] Sat, 22 Aug 2026 18:29:52 UTC (157 KB)
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