Human Human

Research on human-human conversation aims to understand and replicate the complexities of natural dialogue, focusing on improving conversational agents' fluency, engagement, and understanding. Current efforts concentrate on modeling phenomena like lexical entrainment (matching conversational partners' word choices), efficiently processing real-time speech (including speaker diarization and intent detection), and accurately tracking context and topic transitions. These advancements have implications for building more human-like conversational AI, improving the effectiveness of virtual assistants, and enhancing human-robot interaction.