Controlling Backchannels in Streamable Full-duplex Models

Abstract:Backchannels, brief acknowledgements like "uh-huh" produced while the other party may still be talking, are central to natural conversation, but full-duplex spoken dialogue models rarely model them explicitly. We introduce a lightweight backchannel head that predicts, from a full-duplex model's own hidden states, when a backchannel should begin. Once this probability crosses a tunable threshold, a backchannel is force-decoded. Attached to both a 7B (PersonaPlex) and a 1B (F-Actor) model, it generalizes across scale. Probing confirms the hidden states anticipate real human timing, and generation evaluation shows more frequent, better-timed backchannels. Human raters judge the resulting backchannels on par with real ones.

Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)

arXiv:2609.29418 [cs.CL]

(or arXiv:2609.29418v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.29418 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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