SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noise

• URL: http://arxiv.org/abs/2602.12783v1
• Date: Fri, 13 Feb 2026 10:08:27 GMT
• Title: SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noise
• Authors: Yuejie Li, Ke Yang, Yueying Hua, Berlin Chen, Jianhao Nie, Yueping He, Caixin Kang,
• Abstract summary: We present SQuTR, a robustness benchmark for spoken query retrieval.<n>SQuTR aggregates 37,317 unique queries from six commonly used English and Chinese text retrieval datasets.<n>We conduct large-scale evaluations on representative cascaded and end-to-end retrieval systems.
• Score: 11.887069140065774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions [54.34001921326444]
  Speaker verification (SV) models are increasingly integrated into security, personalization, and access control systems.<n>Existing benchmarks evaluate only subsets of these conditions, missing others entirely.<n>We introduce SVeritas, a comprehensive Speaker Verification tasks benchmark suite, assessing SV systems under stressors like recording duration, spontaneity, content, noise, microphone distance, reverberation, channel mismatches, audio bandwidth, codecs, speaker age, and susceptibility to spoofing and adversarial attacks.
  arXiv  Detail & Related papers  (2025-09-21T14:11:16Z)
• HANRAG: Heuristic Accurate Noise-resistant Retrieval-Augmented Generation for Multi-hop Question Answering [25.01360941843264]
  HANRAG is a novel framework designed to efficiently tackle problems of varying complexity.<n> Driven by a powerful revelator, HANRAG routes queries, decomposes them into sub-queries, and filters noise from retrieved documents.<n>Results demonstrate that our framework obtains superior performance in both single-hop and multi-hop question-answering tasks.
  arXiv  Detail & Related papers  (2025-09-08T06:22:38Z)
• Benchmarking Deep Search over Heterogeneous Enterprise Data [73.55304268238474]
  We present a new benchmark for evaluating a form of retrieval-augmented generation (RAG)<n>RAG requires source-aware, multi-hop reasoning over diverse, sparsed, but related sources.<n>We build it using a synthetic data pipeline that simulates business across product planning, development, and support stages.
  arXiv  Detail & Related papers  (2025-06-29T08:34:59Z)
• RARE: Retrieval-Aware Robustness Evaluation for Retrieval-Augmented Generation Systems [33.389969814185214]
  Retrieval-Augmented Generation (RAG) enhances recency and factuality in answers.<n>Existing evaluations rarely test how well RAG systems cope with real-world noise, conflicting between internal and external retrieved contexts, or fast-changing facts.<n>We introduce Retrieval-Aware Robustness Evaluation (RARE), a unified framework and large-scale benchmark that jointly stress-test query and document perturbations over dynamic, time-sensitive corpora.
  arXiv  Detail & Related papers  (2025-06-01T02:42:36Z)
• Dynamic Uncertainty Learning with Noisy Correspondence for Text-Based Person Search [5.4593658173370985]
  Large-scale text-image datasets are created from mismatched pairs.<n>Existing methods often focus on negative samples, which amplify noise.<n>We propose Dynamic Uncertainty co-occurrence and alignment framework.
  arXiv  Detail & Related papers  (2025-05-10T08:35:36Z)
• Efficient Conversational Search via Topical Locality in Dense Retrieval [9.38751103209178]
  We exploit the topical locality inherent in conversational queries to improve response time.<n>By leveraging query embedding similarities, we dynamically restrict the search space to semantically relevant document clusters.<n>Our results show that the proposed system effectively handles complex, multiturn queries with high precision and efficiency.
  arXiv  Detail & Related papers  (2025-04-30T10:56:34Z)
• Noisy Pair Corrector for Dense Retrieval [59.312376423104055]
  We propose a novel approach called Noisy Pair Corrector (NPC) NPC consists of a detection module and a correction module. We conduct experiments on text-retrieval benchmarks Natural Question and TriviaQA, code-search benchmarks StaQC and SO-DS.
  arXiv  Detail & Related papers  (2023-11-07T08:27:14Z)
• Noise-Robust Dense Retrieval via Contrastive Alignment Post Training [89.29256833403167]
  Contrastive Alignment POst Training (CAPOT) is a highly efficient finetuning method that improves model robustness without requiring index regeneration. CAPOT enables robust retrieval by freezing the document encoder while the query encoder learns to align noisy queries with their unaltered root. We evaluate CAPOT noisy variants of MSMARCO, Natural Questions, and Trivia QA passage retrieval, finding CAPOT has a similar impact as data augmentation with none of its overhead.
  arXiv  Detail & Related papers  (2023-04-06T22:16:53Z)
• On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering [13.013751306590303]
  We study the robustness of lexical and dense retrievers against questions with synthetic ASR noise. We create a new dataset with questions voiced by human users and use their transcriptions to show that the retrieval performance can further degrade when dealing with natural ASR noise instead of synthetic ASR noise.
  arXiv  Detail & Related papers  (2022-09-26T18:29:36Z)
• Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting [56.268862325167575]
  We tackle conversational passage retrieval (ConvPR) with query reformulation integrated into a multi-stage ad-hoc IR system. We propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model.
  arXiv  Detail & Related papers  (2020-05-05T14:30:20Z)
• Continuous speech separation: dataset and analysis [52.10378896407332]
  In natural conversations, a speech signal is continuous, containing both overlapped and overlap-free components. This paper describes a dataset and protocols for evaluating continuous speech separation algorithms.
  arXiv  Detail & Related papers  (2020-01-30T18:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.