| title | Real-TurnTurk: A Multimodal Turkish Corpus for Turn-Taking Prediction |
|---|---|
| authors | Ahmet Tuğrul Bayrak, Fatma Nur Korkmaz, Bekir Berker Türker, Mustafa Sertaç Türkel, Alper Kaplan |
| date | 2026-08-22 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.22071 |
| links | abs pdf hf |
| 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. |
cite
@misc{bayrak2026real,
title={Real-TurnTurk: A Multimodal Turkish Corpus for Turn-Taking Prediction},
author={Ahmet Tuğrul Bayrak and Fatma Nur Korkmaz and Bekir Berker Türker and Mustafa Sertaç Türkel and Alper Kaplan},
year={2026},
eprint={2608.22071},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.22071}
}