# MSU-Bench: Towards Understanding the Conversational Multi-talker Scenarios

arXiv: https://arxiv.org/abs/2508.08155 · HTML: https://mlsanity.com/paper/2508.08155/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2025.

## Built on by (most cited first, 10 of 10)

- [EchoMind: An Interrelated Multi-level Benchmark for Evaluating Empathetic Speech Language Models](https://mlsanity.com/paper/2510.22758/) (2025)
- [VoxPrivacy: A Benchmark for Evaluating Interactional Privacy of Speech Language Models](https://mlsanity.com/paper/2601.19956/) (2026)
- [M3-SLU: Evaluating Speaker-Attributed Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2510.19358/) (2025)
- [MSI-Bench: Evaluating Multi-Speaker Voice Interaction for Collaborative AI Agents](https://mlsanity.com/paper/2609.24812/) (2026)
- [Protecting Bystander Privacy via Selective Hearing in Audio LLMs](https://mlsanity.com/paper/2512.06380/) (2025)
- [Cocktail-Talker: Multi-Speaker Dialog Modeling in Noisy Social Environments with Turn Action GRPO](https://mlsanity.com/paper/2607.27756/) (2026)
- [MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant](https://mlsanity.com/paper/2609.13076/) (2026)
- [HEAR Who Said What: Unlocking Speaker-Attributed Reasoning via Counterfactual Voice Grounding](https://mlsanity.com/paper/2608.29120/) (2026)
- [The Eloquence submission for Task 2 of the Interspeech 2026 MLC-SLM challenge](https://mlsanity.com/paper/2609.11724/) (2026)
- [Beyond Dyadic Memory: Interaction-Aware Multimodal Memory with Adaptive Agentic Retrieval for Multi-Party Spoken Conversations](https://mlsanity.com/paper/2609.32522/) (2026)

## Abstract (arXiv)

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.