@inproceedings{zufle-etal-2026-f,
title = "{F}-Actor: Controllable Conversational Behavior in Full-Duplex Models",
author = {Z{\"u}fle, Maike and
Klejch, Ondrej and
Sanders, Nicholas and
Niehues, Jan and
Birch, Alexandra and
Lam, Tsz Kin},
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.242/",
pages = "4904--4921",
ISBN = "979-8-89176-395-1",
abstract = "Spoken conversational systems require more than accurate speech generation to have human-like conversations: to feel natural and engaging, they must produce conversational behaviour that adapts dynamically to the context. Current spoken conversational systems, however, rarely allow such customization, limiting their naturalness and usability. In this work, we present the first open, instruction-following full-duplex conversational speech model that can be trained efficiently under typical academic resource constraints. By keeping the audio encoder frozen and finetuning only the language model, our model requires just 2,000 hours of data, without relying on large-scale pretraining or multi-stage optimization. The model can follow explicit instructions to control speaker voice, conversation topic, conversational behaviour (e.g., backchanneling and interruptions), and dialogue initiation. We propose a single-stage training protocol and systematically analyze design choices. Both the model and training code is released to enable reproducible research on controllable full-duplex speech systems."
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%0 Conference Proceedings
%T F-Actor: Controllable Conversational Behavior in Full-Duplex Models
%A Züfle, Maike
%A Klejch, Ondrej
%A Sanders, Nicholas
%A Niehues, Jan
%A Birch, Alexandra
%A Lam, Tsz Kin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zufle-etal-2026-f
%X Spoken conversational systems require more than accurate speech generation to have human-like conversations: to feel natural and engaging, they must produce conversational behaviour that adapts dynamically to the context. Current spoken conversational systems, however, rarely allow such customization, limiting their naturalness and usability. In this work, we present the first open, instruction-following full-duplex conversational speech model that can be trained efficiently under typical academic resource constraints. By keeping the audio encoder frozen and finetuning only the language model, our model requires just 2,000 hours of data, without relying on large-scale pretraining or multi-stage optimization. The model can follow explicit instructions to control speaker voice, conversation topic, conversational behaviour (e.g., backchanneling and interruptions), and dialogue initiation. We propose a single-stage training protocol and systematically analyze design choices. Both the model and training code is released to enable reproducible research on controllable full-duplex speech systems.
%U https://aclanthology.org/2026.findings-acl.242/
%P 4904-4921
Markdown (Informal)
[F-Actor: Controllable Conversational Behavior in Full-Duplex Models](https://aclanthology.org/2026.findings-acl.242/) (Züfle et al., Findings 2026)
ACL
- Maike Züfle, Ondrej Klejch, Nicholas Sanders, Jan Niehues, Alexandra Birch, and Tsz Kin Lam. 2026. F-Actor: Controllable Conversational Behavior in Full-Duplex Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 4904–4921, San Diego, California, United States. Association for Computational Linguistics.