# Can Large Audio Language Models Understand Audio Well? Speech, Scene and Events Understanding Benchmark for LALMs

arXiv: https://arxiv.org/abs/2509.13148 · HTML: https://mlsanity.com/paper/2509.13148/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2025.

## Built on by (most cited first, 5 of 5)

- [Can large audio language models understand child stuttering speech? speech summarization, and source separation](https://mlsanity.com/paper/2510.20850/) (2025)
- [GlobeAudio: A Multilingual Multicultural Benchmark for Naturalistic Evaluation of Large Audio-Language Models](https://mlsanity.com/paper/2606.08194/) (2026)
- [Tracing Audio Grounding and Answer Selection in Audio LLMs](https://mlsanity.com/paper/2609.04637/) (2026)
- [Long-Term Memory-Guided Enhancement for Target Perception in Audio-Language Models](https://mlsanity.com/paper/2609.36577/) (2026)
- [CARES: A Controlled Synthetic Benchmark of Speaker Reactions to Sound](https://mlsanity.com/paper/2610.10208/) (2026)

## Abstract (arXiv)

Recently, Large Audio Language Models (LALMs) have progressed rapidly, demonstrating their strong efficacy in universal audio understanding through cross-modal integration. To evaluate LALMs' audio understanding performance, researchers have proposed different benchmarks. However, key aspects for real-world interactions are underexplored in existing benchmarks, i.e., audio signals typically contain both speech and non-speech components, and energy levels of these components can vary significantly across different scenarios. Moreover, most benchmarks do not consider the joint understanding of speech, scene, and events within the same audio clip. In this work, we introduce SSEU-Bench, the first versatile audio understanding benchmark that explicitly accounts for energy differences between speech and non-speech audio, with both independent and joint understanding settings for speech, scene, and events. Furthermore, we demonstrate that some LALMs tend to underperform on certain tasks in a joint understanding setting. To address this issue, we introduce Chain-of-Thought, which effectively improves LALMs' joint audio understanding performance by decomposing complex tasks into simpler reasoning steps.