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Controlling Backchannels in Streamable Full-duplex Models

2026-09-24 11:38:40
Maike Z\"ufle, Peter Pol\'ak, Sefik Emre Eskimez, Jan Niehues, Peter Bell, Ond\v{r}ej Klejch
arXiv_CL
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2609.29418

PDF

https://arxiv.org/pdf/2609.29418.pdf

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