SQuTR aggregates 37k queries from six text retrieval datasets, synthesizes speech from 200 speakers, adds 17 noise categories at varying SNR, and shows that even large retrieval models degrade sharply under extreme acoustic noise.
Voiceassistant- eval: Benchmarking ai assistants across listening, speaking, and viewing
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WASIL is a released dataset of Arabic spoken interactions with LLMs that includes audio, ASR outputs, responses, user feedback, and answerability labels to isolate ASR effects.
A survey of Large Audio Language Models that establishes a taxonomy of trustworthiness vulnerabilities and proposes a Defense-in-Depth roadmap for audio intelligence.
citing papers explorer
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SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noise
SQuTR aggregates 37k queries from six text retrieval datasets, synthesizes speech from 200 speakers, adds 17 noise categories at varying SNR, and shows that even large retrieval models degrade sharply under extreme acoustic noise.
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WASIL: In-the-Wild Arabic Spoken Interactions with LLMs
WASIL is a released dataset of Arabic spoken interactions with LLMs that includes audio, ASR outputs, responses, user feedback, and answerability labels to isolate ASR effects.
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A Survey of Large Audio Language Models: Generalization, Trustworthiness, and Outlook
A survey of Large Audio Language Models that establishes a taxonomy of trustworthiness vulnerabilities and proposes a Defense-in-Depth roadmap for audio intelligence.