SQuTR: A robustness benchmark for spoken query retrieval under realistic acoustic noise.
Spoken query retrieval is an important interaction mode in modern information retrieval. However, existing evaluation datasets are often limited to simple queries under constrained noise conditions, making them inadequate for assessing the robustness of spoken query retrieval systems under complex acoustic perturbations. To address this limitation, we present SQuTR, a robustness benchmark for spoken query retrieval that includes a large-scale dataset and a unified evaluation protocol. SQuTR aggregates 37,317 unique queries from six commonly used English and Chinese text retrieval datasets, spanning multiple domains and diverse query types. We synthesize speech using voice profiles from 200 real speakers and mix 17 categories of real-world environmental noise under controlled SNR levels, enabling reproducible robustness evaluation from quiet to highly noisy conditions. Under the unified protocol, we conduct large-scale evaluations on representative cascaded and end-to-end retrieval systems. Experimental results show that retrieval performance decreases as noise increases, with substantially different drops across systems. Even large-scale retrieval models struggle under extreme noise, indicating that robustness remains a critical bottleneck. Overall, SQuTR provides a reproducible testbed for benchmarking and diagnostic analysis, and facilitates future research on robustness in spoken query to text retrieval.
SQuTR bridges the gap between speech recognition and information retrieval by testing end-to-end system robustness under acoustic noise.
The benchmark combines 37,000 queries from six major IR datasets across English and Chinese with rigorous quality control.
Previous benchmarks like SVQ lacked systematic noise control and reproducible acoustic conditions for robust robustness testing.
SVQ queries were mostly simple factoid questions with standardized forms lacking complexity.
Previous benchmarks didn't systematically control noise intensity across graded signal-to-noise ratio levels.
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