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

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
• 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:

• 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 . InProceedings 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 (Koo, IMPACT-SPEECH 2026)

- PDF:
- [https://aclanthology.org/2026.impactspeech-1.3.pdf](https://aclanthology.org/2026.impactspeech-1.3.pdf)