Listener Embeddings

Listener embeddings represent a burgeoning area of research focused on computationally modeling the listener's role in conversation. Current work explores how to generate realistic virtual listeners, predict listener responses like backchannels (e.g., "uh-huh"), and improve human-computer interaction by incorporating listener behavior into models. This involves using neural networks, often incorporating acoustic and lexical features, and leveraging techniques like diffusion models and CLIP scores to achieve better performance on tasks such as predicting backchannels or identifying shared information in collaborative games. The resulting advancements have implications for building more natural and engaging conversational AI systems and a deeper understanding of human-human interaction.