arXiv:2510.05150v3 Announce Type: replace-cross Abstract: Recent advances in spoken dialogue language models (SDLMs) reflect growing interest in shifting from turn-based to full-duplex systems, where the models continuously perceive user speech streams while generating responses. This simultaneous listening and speaking design enables real-time interaction and the agent can handle dynamic conversational behaviors like user barge-in. However, during the listening phase, existing systems keep the agent idle by repeatedly predicting the silence token, which departs from human behavior: we usually
The paper addresses a critical limitation in current spoken dialogue language models, pushing towards more human-like, real-time interaction in an era of rapid AI advancement.
Improving full-duplex systems makes AI interactions feel more natural and efficient, potentially accelerating adoption in customer service, personal assistants, and other interactive AI applications.
This research suggests a shift from 'idle' AI listening phases to continuous, context-aware processing, enabling more dynamic and less jarring conversational experiences.
- · AI assistant developers
- · Customer service platforms
- · Human-computer interaction researchers
- · Voice AI hardware manufacturers
- · Turn-based dialogue system providers
- · Companies with less sophisticated real-time processing
More intuitive and fluid AI-human conversations in various applications.
Increased user satisfaction and reliance on AI interfaces due to reduced conversational friction.
The development of more sophisticated multi-modal AI agents that seamlessly integrate speech with other sensory inputs.
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Read at arXiv cs.AI