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    <abstract>Robust voice agents require exposure to the full diversity of how people interact through speech. However, obtaining enough spoken interactions is prohibitively expensive. Building spoken user simulators that address this requires large-scale spoken task-oriented dialogue (TOD) data encompassing spoken user behaviors, yet existing datasets are limited in scale and domain coverage, with no systematic pipeline for augmenting them. To address this, we introduce SpokenTOD, a spoken TOD dataset of 52,390 dialogues and 1,034 hours of speech augmented with four spoken user behaviors—cross-turn slots, barge-in, disfluency, and emotional prosody—across diverse speakers and domains. Building on SpokenTOD, we present SpokenUS, a spoken user simulator grounded in TOD that decides when to speak through a dedicated turn-taking head. SpokenUS achieves comparable goal coverage to much larger models while substantially outperforming all baselines in human MOS, disclosing slot values gradually across the dialogue as humans do rather than front-loading them. Further analysis confirms that SpokenUS’s spoken behaviors pose meaningful challenges to voice agents, making it a practical tool for evaluating more robust spoken dialogue systems. Our code is available at https://github.com/holi-lab/SpokenUS.</abstract>
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%0 Conference Proceedings
%T SpokenUS: A Spoken User Simulator for Task-Oriented Dialogue
%A Lee, Jonggeun
%A Pyo, Junseong
%A Park, Jeongmin
%A Jo, Yohan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lee-etal-2026-spokenus
%X Robust voice agents require exposure to the full diversity of how people interact through speech. However, obtaining enough spoken interactions is prohibitively expensive. Building spoken user simulators that address this requires large-scale spoken task-oriented dialogue (TOD) data encompassing spoken user behaviors, yet existing datasets are limited in scale and domain coverage, with no systematic pipeline for augmenting them. To address this, we introduce SpokenTOD, a spoken TOD dataset of 52,390 dialogues and 1,034 hours of speech augmented with four spoken user behaviors—cross-turn slots, barge-in, disfluency, and emotional prosody—across diverse speakers and domains. Building on SpokenTOD, we present SpokenUS, a spoken user simulator grounded in TOD that decides when to speak through a dedicated turn-taking head. SpokenUS achieves comparable goal coverage to much larger models while substantially outperforming all baselines in human MOS, disclosing slot values gradually across the dialogue as humans do rather than front-loading them. Further analysis confirms that SpokenUS’s spoken behaviors pose meaningful challenges to voice agents, making it a practical tool for evaluating more robust spoken dialogue systems. Our code is available at https://github.com/holi-lab/SpokenUS.
%U https://aclanthology.org/2026.emnlp-main.87/
%P 2035-2070

Markdown (Informal)

[SpokenUS: A Spoken User Simulator for Task-Oriented Dialogue](https://aclanthology.org/2026.emnlp-main.87/) (Lee et al., EMNLP 2026)

• SpokenUS: A Spoken User Simulator for Task-Oriented Dialogue (Lee et al., EMNLP 2026)

ACL

• Jonggeun Lee, Junseong Pyo, Jeongmin Park, and Yohan Jo. 2026. SpokenUS: A Spoken User Simulator for Task-Oriented Dialogue. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 2035–2070, Budapest, Hungary. Association for Computational Linguistics.