---
title: "RSA-Bench — paper by Zhang et al. (2026) — summary and ideas"
description: "The authors build each item by mixing clean speech with one to four real environmental recordings. 57 references."
authors: ["Yibo Zhang", "Kaiwen Luo", "Liang Lin", "Shilinlu Yan", "Jin Wang", "Yaoqi Guo", "Yitian Chen", "Yalan Qin", "Zhenhong Zhou", "Kun Wang", "Li Sun"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.913/"
doi: "10.18653/v1/2026.findings-acl.913"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2026-rsa-bench-benchmarking-audio-large/"
---
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# RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios

**Authors:** [Yibo Zhang](https://md.research.elamur.ai/free/en/authors/yibo-zhang/), [Kaiwen Luo](https://md.research.elamur.ai/free/en/authors/kaiwen-luo/), [Liang Lin](https://md.research.elamur.ai/free/en/authors/liang-lin/), [Shilinlu Yan](https://md.research.elamur.ai/free/en/authors/shilinlu-yan/), [Jin Wang](https://md.research.elamur.ai/free/en/authors/jin-wang/), Yaoqi Guo, [Yitian Chen](https://md.research.elamur.ai/free/en/authors/yitian-chen/), Yalan Qin, [Zhenhong Zhou](https://md.research.elamur.ai/free/en/authors/zhenhong-zhou/), [Kun Wang](https://md.research.elamur.ai/free/en/authors/kun-wang/) and [Li Sun](https://md.research.elamur.ai/free/en/authors/li-sun/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.913/) · [PDF](https://aclanthology.org/2026.findings-acl.913.pdf) · [DOI 10.18653/v1/2026.findings-acl.913](https://doi.org/10.18653/v1/2026.findings-acl.913) · [OpenAlex](https://openalex.org/W7166819457)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Audio Large Models (ALLMs) score well on clean benchmarks, but their behavior in realistic noisy settings has rarely been measured. Earlier robustness tests mostly relied on synthetic Gaussian noise or a single interfering source. RSA-Bench addresses this with more than 100,000 test items covering six tasks, from basic perception (ASR, gender and emotion recognition) to higher-level reasoning (spoken QA, math).

The authors build each item by mixing clean speech with one to four real environmental recordings. These come from the ESC subset of DynamicSuperb and are grouped into four scenes: Pasture, Extreme Weather, Classroom and Outdoors. Noise clips are looped or trimmed to match the speech length and scaled to the same RMS energy as the speech.

Across the evaluated models, accuracy falls as more noise sources are added. Reasoning tasks degrade far more sharply than perception tasks, and speech-like background sounds, such as children playing, cause more damage than mechanical noise. Applying standard speech enhancement before inference usually makes results worse. The authors argue that robustness must therefore be built into the models during training rather than added through preprocessing.

## Key points
- **Benchmark design:** RSA-Bench has 6 tasks × 4 scenes × 4 complexity levels (K = 1–4 noise sources), giving 16 variants per clean sample. The largest tasks are ASR and gender recognition (2,600 base samples each), and clean audio comes from LibriSpeech, IEMOCAP, MELD, SpokenMQA, SLUE Phase-2 and OpenHermes.
- **Perception–cognition gap:** Low-level skills such as gender recognition stay relatively stable under noise, while reasoning tasks collapse. For example, SeaLLMs drops from 62.00 on clean audio to 10.00 at K = 3.
- **Denoising methods tested:** noisereduce, RNNoise, Audio-Denoising (wavelet-based) and DeepFilterNet were applied to Qwen2-Audio, MERaLiON and StepAudio2 in the Pasture and Classroom scenes.
- **Denoising often hurts:** At K = 1, Qwen2-Audio's ASR WER rises from 4.24% on noisy audio to 12.90% after noisereduce. At K = 4, noisereduce pushes WER to 55.74%, compared with 14.63% on the unprocessed noisy audio.
- **Deep learning vs. traditional enhancement:** Neural enhancers such as DeepFilterNet do less damage than traditional signal-processing methods, but none of them beat the raw noisy input.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Yibo Zhang, Kaiwen Luo, Liang Lin, Shilinlu Yan, Jin Wang, Yaoqi Guo, Yitian Chen, Yalan Qin, Zhenhong Zhou, Kun Wang and Li Sun. 2026. RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2026rsa,
  title = {{RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios}},
  author = {Yibo Zhang, Kaiwen Luo, Liang Lin, Shilinlu Yan, Jin Wang, Yaoqi Guo, Yitian Chen, Yalan Qin, Zhenhong Zhou, Kun Wang and Li Sun},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.913},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.913}
}
```

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- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/48/)

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*Updated 2 November 2026*