{"ID":2883855,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08155","arxiv_id":"2508.08155","title":"MSU-Bench: Towards Understanding the Conversational Multi-talker Scenarios","abstract":"Spoken Language Understanding (SLU) has progressed from traditional single-task methods to large audio language model (LALM) solutions. Yet, most existing speech benchmarks focus on single-speaker or isolated tasks, overlooking the challenges posed by multi-speaker conversations that are common in real-world scenarios. We introduce MSU-Bench, a comprehensive benchmark for evaluating multi-speaker conversational understanding with a speaker-centric design. Our hierarchical framework covers four progressive tiers: single-speaker static attribute understanding, single-speaker dynamic attribute understanding, multi-speaker background understanding, and multi-speaker interaction understanding. This structure ensures all tasks are grounded in speaker-centric contexts, from basic perception to complex reasoning across multiple speakers. By evaluating state-of-the-art models on MSU-Bench, we demonstrate that as task complexity increases across the benchmark's tiers, all models exhibit a significant performance decline. We also observe a persistent capability gap between open-source models and closed-source commercial ones, particularly in multi-speaker interaction reasoning. These findings validate the effectiveness of MSU-Bench for assessing and advancing conversational understanding in realistic multi-speaker environments. Demos can be found in the supplementary material.","short_abstract":"Spoken Language Understanding (SLU) has progressed from traditional single-task methods to large audio language model (LALM) solutions. Yet, most existing speech benchmarks focus on single-speaker or isolated tasks, overlooking the challenges posed by multi-speaker conversations that are common in real-world scenarios....","url_abs":"https://arxiv.org/abs/2508.08155","url_pdf":"https://arxiv.org/pdf/2508.08155v1","authors":"[\"Shuai Wang\",\"Zhaokai Sun\",\"Zhennan Lin\",\"Chengyou Wang\",\"Zhou Pan\",\"Lei Xie\"]","published":"2025-08-11T16:31:32Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"cs.SD\"]","methods":"[\"Language Model\"]","has_code":false}