研究人员正在开发改进全双工口语对话模型的新方法，这些模型允许同时进行听和说。一种方法 PERSIST 引入了一个记忆系统，该系统跟踪谁在何时说了什么，从而显著降低了检索延迟。另一个关注点是这些模型在相互交互时的轮流发言行为，研究表明它们的时序是耦合的，但与人类对话相比通常会延迟。正在提出 DyaFDB 和 HiPLEX 等新框架来评估和改进这些模型，考虑它们的二元交互和分层策略分解，以获得更好的时序和内容协调。 AI
影响 全双工对话模型的进步可能带来更自然、响应更快的语音助手和 AI 代理。
排序理由 多篇研究论文介绍了全双工口语对话系统的新模型、框架和基准。
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arXiv:2610.08887v1 Announce Type: new Abstract: Full-duplex voice agents need to modulate emotion and delivery during real-time conversations, when de-escalating a complaint, carrying urgency in dispatch, softening a clinical result. Emotion and delivery control is well studied f…
arXiv:2610.07725v1 Announce Type: new Abstract: Modern voice assistants may be shared by multiple users and should be able to answer questions about earlier conversations such as "When did I originally plan to leave?" or adapt their behavior to individual users based on past inte…
arXiv:2610.08683v1 Announce Type: new Abstract: Full-duplex speech models are trained to converse with a person, but they are increasingly made to converse with each other, in self-play data generation, agent societies, and model-based evaluation. In that loop no human absorbs a …
arXiv:2610.08125v1 Announce Type: cross Abstract: Full-duplex spoken dialogue models listen and speak at the same time, enabling voice agents to have natural, low-latency interactions that turn-based systems cannot offer. However, they are commonly evaluated against single-sided …
As human--AI interactions become more conversational, full-duplex speech language models capable of natural real-time dialogue are growing in importance. Beyond generating appropriate responses, these models must coordinate turn-taking, backchanneling, and floor management in rea…