Turn Taking Prediction

Turn-taking prediction focuses on accurately anticipating when speakers will change in conversations, a crucial aspect of building natural and engaging human-computer interaction systems. Current research heavily utilizes transformer-based architectures, often incorporating multimodal data (audio, video, text) and advanced techniques like contrastive learning to improve prediction accuracy, particularly in complex multi-party scenarios. These advancements are significant for improving the fluidity and naturalness of human-robot dialogue, virtual assistants, and other applications requiring real-time conversational understanding. The development of robust and efficient turn-taking prediction models is driving progress in areas such as human-robot interaction and conversational AI.