Abstract
Speech Large Language Models (Speech LLMs) are increasingly deployed in real-world acoustic environments, yet it remains unclear which measurable properties of background sounds best explain performance degradation. We present AIP-Speech, an inference-only benchmark that evaluates Speech LLM robustness under controlled mixtures of realworld background recordings. Rather than comparing robustness across background categories alone, we characterize each recording using interpretable acoustic descriptors and examine their associations with degradation. Across five open Speech LLMs, we find that simple acoustic descriptors—including Zero Crossing Rate, Spectral Centroid, and Spectral Rolloff—consistently exhibit strong associations with degradation. We further quantify robustness disparities across accents and evaluate whether inference-time prompting improves resilience under adverse acoustic conditions. AIP-Speech provides a reproducible benchmark and descriptor-annotated background corpus for analyzing Speech LLM robustness.
- Anthology ID:
- 2026.impactspeech-1.2
- Volume:
- Proceedings of the IMPACT-SPEECH Workshop: Identifying, Measuring, Preventing, and Assessing Consequences of Bias in Speech LLMs
- Month:
- October
- Year:
- 2026
- Address:
- Budapest, Hungary
- Editors:
- Ravi Shekhar ,Monorama Swain ,Jagabandhu Mishra ,Sandipan Dhar ,Haralambos Mouratidis ,Matthew Purver
- Venues:
- [IMPACT-SPEECH](https://aclanthology.org/venues/impactspeech/) |[WS](https://aclanthology.org/venues/ws/)
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10–19
- Language:
- URL:
- [https://aclanthology.org/2026.impactspeech-1.2/](https://aclanthology.org/2026.impactspeech-1.2/)
- DOI:
- Cite (ACL):
- Aiden Dsouza, Shreyansh Mishra, Yashraj Suneja, Rohan Swain, Samyak Borkar, Arjun Jaishankar, and Tanmay Urade. 2026. AIP-Speech: Auditing Speech LLM Robustness to Background Interference . InProceedings of the IMPACT-SPEECH Workshop: Identifying, Measuring, Preventing, and Assessing Consequences of Bias in Speech LLMs , pages 10–19, Budapest, Hungary. Association for Computational Linguistics.
- Cite (Informal):
- [AIP-Speech: Auditing Speech LLM Robustness to Background Interference](https://aclanthology.org/2026.impactspeech-1.2/) (Dsouza et al., IMPACT-SPEECH 2026)
- PDF:
- [https://aclanthology.org/2026.impactspeech-1.2.pdf](https://aclanthology.org/2026.impactspeech-1.2.pdf)