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New AVCC corpus targets conversational AI turn-taking prediction

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced the Audio-Visual Conversation Corpus (AVCC), a new dataset designed to improve the prediction of turn-taking in conversations. This corpus, spanning 31 hours of audio and video from two- and three-person online discussions, is intended for use with single-camera and microphone setups, mimicking the capabilities of conversational agents and social robots. The AVCC includes a benchmark with speaker-disjoint test partitions, word-level transcripts, and a human reference benchmark to evaluate model performance against human capabilities in predicting the next speaker. AI

IMPACT This new corpus could advance the development of more natural and efficient conversational AI agents by improving their ability to predict and manage turn-taking.

RANK_REASON Publication of a new academic corpus and benchmark for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

New AVCC corpus targets conversational AI turn-taking prediction

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Publication of a new academic corpus and benchmark for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Haotian Qi, Gabriel Skantze ·

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

    arXiv:2611.13957v1 Announce Type: new 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-spe…

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