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New Turkish multimodal corpus aids conversational AI turn-taking prediction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Real-TurnTurk, a new multimodal Turkish conversational dataset designed to improve turn-taking prediction in synchronous dialog systems. The corpus includes synchronized video, audio, and transcriptions of unscripted dyadic interactions. To address turn-taking dynamics, a Genetic Algorithm was used to optimize decision rules based on visual, acoustic, and linguistic features, employing a hybrid AND-OR rule representation for turn transition cues. AI

IMPACT This dataset could advance research in multimodal conversational AI, particularly for languages beyond English, by improving turn-taking prediction.

RANK_REASON The cluster contains an academic paper detailing a new dataset and methodology for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New Turkish multimodal corpus aids conversational AI turn-taking prediction

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The cluster contains an academic paper detailing a new dataset and methodology for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ahmet Tu\u{g}rul Bayrak, Fatma Nur Korkmaz, Bekir Berker T\"urker, Mustafa Serta\c{c} T\"urkel, Alper Kaplan ·

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

    arXiv:2608.22071v1 Announce Type: cross 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 tu…

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