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@inproceedings{dsouza-etal-2026-aip,
    title = "{AIP}-Speech: Auditing Speech {LLM} Robustness to Background Interference",
    author = "Dsouza, Aiden  and
      Mishra, Shreyansh  and
      Suneja, Yashraj  and
      Swain, Rohan  and
      Borkar, Samyak  and
      Jaishankar, Arjun  and
      Urade, Tanmay",
    editor = "Shekhar, Ravi  and
      Swain, Monorama  and
      Mishra, Jagabandhu  and
      Dhar, Sandipan  and
      Mouratidis, Haralambos  and
      Purver, Matthew",
    booktitle = "Proceedings of the {IMPACT}-{SPEECH} Workshop: Identifying, Measuring, Preventing, and Assessing Consequences of Bias in Speech {LLM}s",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.impactspeech-1.2/",
    pages = "10--19",
    ISBN = "979-8-89176-479-8",
    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."
}

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        <namePart type="given">Rohan</namePart>
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        <namePart type="given">Arjun</namePart>
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            <title>Proceedings of the IMPACT-SPEECH Workshop: Identifying, Measuring, Preventing, and Assessing Consequences of Bias in Speech LLMs</title>
        </titleInfo>
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            <namePart type="given">Ravi</namePart>
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            <namePart type="given">Matthew</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T AIP-Speech: Auditing Speech LLM Robustness to Background Interference
%A Dsouza, Aiden
%A Mishra, Shreyansh
%A Suneja, Yashraj
%A Swain, Rohan
%A Borkar, Samyak
%A Jaishankar, Arjun
%A Urade, Tanmay
%Y Shekhar, Ravi
%Y Swain, Monorama
%Y Mishra, Jagabandhu
%Y Dhar, Sandipan
%Y Mouratidis, Haralambos
%Y Purver, Matthew
%S Proceedings of the IMPACT-SPEECH Workshop: Identifying, Measuring, Preventing, and Assessing Consequences of Bias in Speech LLMs
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-479-8
%F dsouza-etal-2026-aip
%X 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.
%U https://aclanthology.org/2026.impactspeech-1.2/
%P 10-19

Markdown (Informal)

[AIP-Speech: Auditing Speech LLM Robustness to Background Interference](https://aclanthology.org/2026.impactspeech-1.2/) (Dsouza et al., IMPACT-SPEECH 2026)

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. In Proceedings 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.