M3-SLU: Evaluating Speaker-Attributed Reasoning in Multimodal Large Language Models

• URL: http://arxiv.org/abs/2510.19358v1
• Date: Wed, 22 Oct 2025 08:28:43 GMT
• Title: M3-SLU: Evaluating Speaker-Attributed Reasoning in Multimodal Large Language Models
• Authors: Yejin Kwon, Taewoo Kang, Hyunsoo Yoon, Changouk Kim,
• Abstract summary: We present M3-SLU, a new multimodal large language model (MLLM) benchmark for evaluating multi-speaker, multi-turn spoken language understanding.<n>M3-SLU is built from four open corpora (CHiME-6, MELD, MultiDialog, and AMI) and comprises over 12,000 validated instances with paired audio, transcripts, and metadata.<n>Results show that while models can capture what was said, they often fail to identify who said it, revealing a key gap in speaker-aware dialogue understanding.
• Score: 15.324265847938813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present M3-SLU, a new multimodal large language model (MLLM) benchmark for evaluating multi-speaker, multi-turn spoken language understanding. While recent models show strong performance in speech and text comprehension, they still struggle with speaker-attributed reasoning, the ability to understand who said what and when in natural conversations. M3-SLU is built from four open corpora (CHiME-6, MELD, MultiDialog, and AMI) and comprises over 12,000 validated instances with paired audio, transcripts, and metadata. It includes two tasks: (1) Speaker-Attributed Question Answering and (2) Speaker Attribution via Utterance Matching. We provide baseline results for both cascaded pipelines and end-to-end MLLMs, evaluated using an LLM-as-Judge and accuracy metrics. Results show that while models can capture what was said, they often fail to identify who said it, revealing a key gap in speaker-aware dialogue understanding. M3-SLU offers as a challenging benchmark to advance research in speaker-aware multimodal understanding.

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