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

TL;DR

M3-SLU introduces a comprehensive benchmark for evaluating multimodal large language models' ability to understand multi-speaker conversations, highlighting significant challenges in speaker attribution despite advances in speech and text comprehension.

Contribution

The paper presents M3-SLU, a new benchmark dataset and evaluation framework specifically designed to assess speaker-attributed reasoning in multimodal large language models.

Findings

Models excel at understanding what was said.

Models struggle with identifying who said it.

Speaker attribution remains a key challenge in multimodal dialogue understanding.

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.