@inproceedings{zhang-etal-2025-omnicharacter,
title = "{O}mni{C}haracter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction",
author = "Zhang, Haonan and
Luo, Run and
Liu, Xiong and
Wu, Yuchuan and
Lin, Ting-En and
Zeng, Pengpeng and
Qu, Qiang and
Fang, Feiteng and
Yang, Min and
Gao, Lianli and
Song, Jingkuan and
Huang, Fei and
Li, Yongbin",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1276/",
doi = "10.18653/v1/2025.acl-long.1276",
pages = "26318--26331",
ISBN = "979-8-89176-251-0",
abstract = "Role-Playing Agents (RPAs), benefiting from large language models, is an emerging interactive AI system that simulates roles or characters with diverse personalities. However, existing methods primarily focus on mimicking dialogues among roles in textual form, neglecting the role{'}s voice traits (e.g., voice style and emotions) as playing a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios. Towards this goal, we propose OmniCharacter, a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency. Specifically, OmniCharacter enables agents to consistently exhibit role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses. To align the model with speech-language scenarios, we construct a dataset named OmniCharacter-10K, which involves more distinctive characters (20), richly contextualized multi-round dialogue (10K), and dynamic speech response (135K). Experimental results showcase that our method yields better responses in terms of both content and style compared to existing RPAs and mainstream speech-language models, with a response latency as low as 289ms."
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<abstract>Role-Playing Agents (RPAs), benefiting from large language models, is an emerging interactive AI system that simulates roles or characters with diverse personalities. However, existing methods primarily focus on mimicking dialogues among roles in textual form, neglecting the role’s voice traits (e.g., voice style and emotions) as playing a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios. Towards this goal, we propose OmniCharacter, a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency. Specifically, OmniCharacter enables agents to consistently exhibit role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses. To align the model with speech-language scenarios, we construct a dataset named OmniCharacter-10K, which involves more distinctive characters (20), richly contextualized multi-round dialogue (10K), and dynamic speech response (135K). Experimental results showcase that our method yields better responses in terms of both content and style compared to existing RPAs and mainstream speech-language models, with a response latency as low as 289ms.</abstract>
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%0 Conference Proceedings
%T OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction
%A Zhang, Haonan
%A Luo, Run
%A Liu, Xiong
%A Wu, Yuchuan
%A Lin, Ting-En
%A Zeng, Pengpeng
%A Qu, Qiang
%A Fang, Feiteng
%A Yang, Min
%A Gao, Lianli
%A Song, Jingkuan
%A Huang, Fei
%A Li, Yongbin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhang-etal-2025-omnicharacter
%X Role-Playing Agents (RPAs), benefiting from large language models, is an emerging interactive AI system that simulates roles or characters with diverse personalities. However, existing methods primarily focus on mimicking dialogues among roles in textual form, neglecting the role’s voice traits (e.g., voice style and emotions) as playing a crucial effect in interaction, which tends to be more immersive experiences in realistic scenarios. Towards this goal, we propose OmniCharacter, a first seamless speech-language personality interaction model to achieve immersive RPAs with low latency. Specifically, OmniCharacter enables agents to consistently exhibit role-specific personality traits and vocal traits throughout the interaction, enabling a mixture of speech and language responses. To align the model with speech-language scenarios, we construct a dataset named OmniCharacter-10K, which involves more distinctive characters (20), richly contextualized multi-round dialogue (10K), and dynamic speech response (135K). Experimental results showcase that our method yields better responses in terms of both content and style compared to existing RPAs and mainstream speech-language models, with a response latency as low as 289ms.
%R 10.18653/v1/2025.acl-long.1276
%U https://aclanthology.org/2025.acl-long.1276/
%U https://doi.org/10.18653/v1/2025.acl-long.1276
%P 26318-26331
Markdown (Informal)
[OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction](https://aclanthology.org/2025.acl-long.1276/) (Zhang et al., ACL 2025)
ACL
- Haonan Zhang, Run Luo, Xiong Liu, Yuchuan Wu, Ting-En Lin, Pengpeng Zeng, Qiang Qu, Feiteng Fang, Min Yang, Lianli Gao, Jingkuan Song, Fei Huang, and Yongbin Li. 2025. OmniCharacter: Towards Immersive Role-Playing Agents with Seamless Speech-Language Personality Interaction. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 26318–26331, Vienna, Austria. Association for Computational Linguistics.