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license: cc-by-sa-4.0 |
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task_categories: |
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- text-retrieval |
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- automatic-speech-recognition |
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language: |
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- en |
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- zh |
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tags: |
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- spoken-query-retrieval |
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- information-retrieval |
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- audio-text-retrieval |
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- mteb |
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- c-mteb |
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- robustness |
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pretty_name: SQuTR |
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size_categories: |
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- 10K<n<100K |
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--- |
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# SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval |
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[](https://github.com/ttoyekk1a/SQuTR-Spoken-Query-to-Text-Retrieval) |
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[](https://arxiv.org/abs/2602.12783) |
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**SQuTR** (Spoken Query-to-Text Retrieval) is a large-scale bilingual benchmark designed to evaluate the robustness of information retrieval systems under realistic acoustic perturbations. |
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While speech interaction is becoming a primary interface for IR systems, performance often degrades significantly in noisy environments. SQuTR provides a standardized framework featuring **37,317** complex queries across **6 domains**, synthesized with **200 real speakers**, and evaluated under **4 graded noise levels**. |
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--- |
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## 🌟 Key Features |
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* **Bilingual & Multi-Domain:** Includes 6 subsets from MTEB and C-MTEB covering Wikipedia, Finance, Medical, and Encyclopedia domains. |
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* **High-Fidelity Synthesis:** Generated using **CosyVoice-3** with diverse speaker profiles, totaling **190.4 hours** of audio. |
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* **Robustness Evaluation:** Explicitly models four acoustic conditions: **Clean, Low Noise (20dB), Medium Noise (10dB), and High Noise (0dB)**. |
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* **MTEB Compatibility:** Follows standard JSONL/BEIR formatting for seamless integration into modern retrieval pipelines. |
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--- |
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## 📂 Dataset Structure |
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The dataset is organized by language and subset. Each subset (e.g., `fiqa`) contains the original text documents and the synthesized audio queries under different SNR conditions. |
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```text |
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SQuTR/ |
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└── source_data/ |
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├── en/ (English Datasets: fiqa, hotpotqa, nq) |
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│ └── [subset_name]/ |
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│ ├── audio_clean/ # Clean original audio files (.wav) |
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│ ├── audio_noise_snr_0/ # Audio with 0dB Signal-to-Noise Ratio |
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│ ├── audio_noise_snr_10/ # Audio with 10dB Signal-to-Noise Ratio |
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│ ├── audio_noise_snr_20/ # Audio with 20dB Signal-to-Noise Ratio |
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│ ├── qrels/ # Query relevance judgments (TSV/JSONL) |
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│ ├── corpus.jsonl # Text corpus documents |
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│ ├── queries.jsonl # Original text queries |
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│ ├── queries_with_audio_clean.jsonl # Metadata mapping text to clean audio |
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│ ├── queries_with_audio_noise_snr_0.jsonl # Metadata for 0dB noise queries |
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│ ├── queries_with_audio_noise_snr_10.jsonl # Metadata for 10dB noise queries |
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│ └── queries_with_audio_noise_snr_20.jsonl # Metadata for 20dB noise queries |
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└── zh/ (Chinese Datasets: DuRetrieval, MedicalRetrieval, T2Retrieval) |
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└── [subset_name]/ |
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└── (Same structure as above) |
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``` |
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--- |
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## 💾 How to Use the Dataset |
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You can download the dataset directly from this Hugging Face repository. To use the evaluation scripts, please refer to our [GitHub Repository](https://github.com/ttoyekk1a/SQuTR-Spoken-Query-to-Text-Retrieval). |
