{"ID":2864816,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23435","arxiv_id":"2509.23435","title":"AudioRole: An Audio Dataset for Character Role-Playing in Large Language Models","abstract":"The creation of high-quality multimodal datasets remains fundamental for advancing role-playing capabilities in large language models (LLMs). While existing works predominantly focus on text-based persona simulation, Audio Role-Playing (ARP) presents unique challenges due to the need for synchronized alignment of semantic content and vocal characteristics. To address this gap, we propose AudioRole, a meticulously curated dataset from 13 TV series spanning 1K+ hours with 1M+ character-grounded dialogues, providing synchronized audio-text pairs annotated with speaker identities and contextual metadata. In addition, to demonstrate the effectiveness of the dataset, we introduced ARP-Eval, a dual-aspect evaluation framework that assesses both response quality and role fidelity. Empirical validation showing GLM-4-Voice trained on AudioRole (which we called ARP-Model) achieve an average Acoustic Personalization score of 0.31, significantly outperforming the original GLM-4-voice and the more powerful model MiniCPM-O-2.6, which specifically supports role-playing in one-shot scenarios. The ARP-Model also achieves a Content Personalization score of 0.36, surpassing the untrained original model by about 38% and maintaining the same level as MiniCPM-O-2.6. AudioRole features dialogues from over 115 main characters, 6 trained ARP-Models that role-play different characters, and evaluation protocols. Together, they provide an essential resource for advancing audio-grounded role-playing research.","short_abstract":"The creation of high-quality multimodal datasets remains fundamental for advancing role-playing capabilities in large language models (LLMs). While existing works predominantly focus on text-based persona simulation, Audio Role-Playing (ARP) presents unique challenges due to the need for synchronized alignment of seman...","url_abs":"https://arxiv.org/abs/2509.23435","url_pdf":"https://arxiv.org/pdf/2509.23435v2","authors":"[\"Wenyu Li\",\"Xiaoqi Jiao\",\"Yi Chang\",\"Guangyan Zhang\",\"Yiwen Guo\"]","published":"2025-09-27T18:08:51Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.AI\",\"cs.MM\",\"eess.AS\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}