CCDb+: Enhanced Annotations and Multi-Modal Benchmark for Natural Dyadic Conversations

摘要

Backchannel signals play a critical role in social interaction, expressing attentiveness, agreement, and emotion in both human and human-agent conversations. However, few multi-modal databases exist in this area due to the complexity of categorisation and the high cost of precise timing, especially in naturalistic dyadic conversations. To address these challenges, we introduce CCDb+ (Cardiff Conversation Database +) an enhanced version of CCDb, with 25 newly annotated conversations and corrections to 14 previously annotated conversations, along with thorough consistency checks to ensure annotation reliability. Additionally, we propose a multi-modal process for backchannel detection as a baseline, showing that both visual and acoustic cues contribute significantly to understanding backchannel behaviour. Recognising that backchannel signals often intersect with other social cues, we introduce several detection sub-tasks-such as smile, nodding, and agreement-with baseline results for each. Finally, we demonstrate multi-modal paradigms for nuanced signals like nodding and thinking. The database and associated annotations are publicly available at https://huggingface.co/datasets/CardiffVisualComputing/CCDb.