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The paper introduces Duplex-MPE, a benchmark for evaluating AI assistants in multi-party, full-duplex dialogues with 2,000 scenarios, assessing models on response initiation, accuracy, and silence preservation.
This paper proposes a decoupled data approach to improve turn-taking in full-duplex dialogue by learning from real spoken dialogues while using text for semantics, leveraging a neural finite state machine framework to enhance naturalness and preserve semantic capabilities.
This paper proposes a generalized style-aware full-duplex framework with a lightweight turn controller LPS-TC, introduces a large-scale dataset WildTurn for real-world conversations, and presents a two-tier evaluation scheme to enhance proactive spoken interactions and response quality in dialogue systems.
PersonaKit is an open-source web platform designed for rapid prototyping and user testing of diverse personas in full-duplex dialogue systems. It allows researchers to configure persona-specific turn-taking behaviors via JSON and conduct A/B surveys to evaluate sociolinguistic interactions.