# Can Large Audio-Language Models Ignore Multilingual Distractors? An Evaluation of Their Selective Auditory Attention Capabilities

> Source: <https://aclanthology.org/2026.impactspeech-1.3/>
> Published: 2026-10-24 00:00:00+00:00

##### Abstract

Robust selective auditory attention under multilingual interference is critical for reliable LALM deployment. We introduce MUSI, a cocktail party-inspired controlled diagnostic evaluation of source-grounded spokenlanguage understanding and reasoning. Each item pairs an English target dialogue with a plausible distractor in English, Spanish, Korean, or Chinese, and evaluates models under (1) single-stream, (2) separation-based, and (3) end-to-end cocktail party settings across controlled SNRs. Across four open-weight and two closed-source LALMs, we find modeldependent language-conditioned variation and heightened vulnerability at adverse SNRs. Errors are dominated by distractor-grounded source confusion, while separation reduces acoustic overlap but often leaves source attribution unresolved. These findings highlight selective auditory attention as a primary capability for reliable LALM deployment.
- Anthology ID:
- 2026.impactspeech-1.3
- Volume:
- [Proceedings of the IMPACT-SPEECH Workshop: Identifying, Measuring, Preventing, and Assessing Consequences of Bias in Speech LLMs](https://aclanthology.org/volumes/2026.impactspeech-1/)
- Month:
- October
- Year:
- 2026
- Address:
- Budapest, Hungary
- Editors:
- [Ravi Shekhar](https://aclanthology.org/people/ravi-shekhar/) ,[Monorama Swain](https://aclanthology.org/people/monorama-swain/) ,[Jagabandhu Mishra](https://aclanthology.org/people/jagabandhu-mishra/unverified/) ,[Sandipan Dhar](https://aclanthology.org/people/sandipan-dhar/unverified/) ,[Haralambos Mouratidis](https://aclanthology.org/people/haralambos-mouratidis/) ,[Matthew Purver](https://aclanthology.org/people/matthew-purver/)
- Venues:
- [IMPACT-SPEECH](https://aclanthology.org/venues/impactspeech/) |[WS](https://aclanthology.org/venues/ws/)
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 20–32
- Language:
- URL:
- [https://aclanthology.org/2026.impactspeech-1.3/](https://aclanthology.org/2026.impactspeech-1.3/)
- DOI:
- Cite (ACL):
- Heejoon Koo. 2026. [Can Large Audio-Language Models Ignore Multilingual Distractors? An Evaluation of Their Selective Auditory Attention Capabilities](https://aclanthology.org/2026.impactspeech-1.3/) . In*Proceedings of the IMPACT-SPEECH Workshop: Identifying, Measuring, Preventing, and Assessing Consequences of Bias in Speech LLMs* , pages 20–32, Budapest, Hungary. Association for Computational Linguistics.
- Cite (Informal):
- [Can Large Audio-Language Models Ignore Multilingual Distractors? An Evaluation of Their Selective Auditory Attention Capabilities](https://aclanthology.org/2026.impactspeech-1.3/) (Koo, IMPACT-SPEECH 2026)
- PDF:
- [https://aclanthology.org/2026.impactspeech-1.3.pdf](https://aclanthology.org/2026.impactspeech-1.3.pdf)