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

arXiv: https://arxiv.org/abs/2608.22071 · HTML: https://mlsanity.com/paper/2608.22071/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 22 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Moshi: a speech-text foundation model for real-time dialogue](https://mlsanity.com/paper/2410.00037/) (2024)
- [Voice Activity Projection: Self-supervised Learning of Turn-taking Events](https://mlsanity.com/paper/2205.09812/) (2022)
- [Py-Feat: Python Facial Expression Analysis Toolbox](https://mlsanity.com/paper/2104.03509/) (2021)
- [Real-time and Continuous Turn-taking Prediction Using Voice Activity Projection](https://mlsanity.com/paper/2401.04868/) (2024)
- [Multimodal Continuous Turn-Taking Prediction Using Multiscale RNNs](https://mlsanity.com/paper/1808.10785/) (2018)
- [Predicting Turn-Taking and Backchannel in Human-Machine Conversations Using Linguistic, Acoustic, and Visual Signals](https://mlsanity.com/paper/2505.12654/) (2025)
- [Syn-TurnTurk: A Synthetic Dataset for Turn-Taking Prediction in Turkish Dialogues](https://mlsanity.com/paper/2604.13620/) (2026)

## Abstract (arXiv)

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.