# AVCC: Audio-Visual Conversation Corpus for Turn-Taking Prediction with a Human Benchmark

**Authors:** Haotian Qi, Gabriel Skantze  
**Published:** 10 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.13957) · [PDF](https://arxiv.org/pdf/2611.13957) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.13957.html)

## Abstract

} Predicting who will speak next, and when, is a prerequisite for conversational agents and social robots that take turns without waiting for silence. Multiparty next-speaker prediction (NSP) has been studied on corpora with per-speaker sensors, each with its own event definitions. No public benchmark covers the single camera and microphone a robot has, and no model has been compared against how well people perform the same prediction. We turn AVCC, a 31-hour audio-visual corpus of two- and three-speaker online conversation recorded with a single static camera and microphone, into a public NSP benchmark. We add a speaker-disjoint seven-video test partition with 865 hold and 865 shift events at mutual-silence gaps, word-level forced-aligned transcripts for the whole corpus, and a human reference benchmark collected at the same events. We score an audio-only Role-Relative VAP model (RoleVAP) and the multimodal MuVAP model. Labels, transcripts, per-response human data, and the scripts that reproduce every number are released.

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