README.md

metadata

license: cc-by-sa-4.0
task_categories:
  - text-retrieval
  - feature-extraction
  - automatic-speech-recognition
language:
  - en
  - zh
tags:
  - spoken-query-retrieval
  - information-retrieval
  - audio-text-retrieval
  - mteb
  - audio
  - c-mteb
  - robustness
pretty_name: SQuTR
size_categories:
  - 10K<n<100K

🏆 Recognition

• [2026-05] SQuTR📄 was ACCEPTED to SIGIR 2026! 🎉
• [2026-02] SQuTR was featured as the #1 Paper of the Day on Hugging Face Daily Papers!

SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval

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.

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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🌟 Key Features

• Bilingual & Multi-Domain: Includes 6 subsets from MTEB and C-MTEB covering Wikipedia, Finance, Medical, and Encyclopedia domains.
• High-Fidelity Synthesis: Generated using CosyVoice-3 with diverse speaker profiles, totaling 190.4 hours of audio.
• Robustness Evaluation: Explicitly models four acoustic conditions: Clean, Low Noise (20dB), Medium Noise (10dB), and High Noise (0dB).
• MTEB Compatibility: Follows standard JSONL/BEIR formatting for seamless integration into modern retrieval pipelines.

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📂 Dataset Structure

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.

SQuTR/
└── source_data/
    ├── en/ (English Datasets: fiqa, hotpotqa, nq)
    │   └── [subset_name]/
    │       ├── audio_clean/              # Clean original audio files (.wav)
    │       ├── audio_noise_snr_0/        # Audio with 0dB Signal-to-Noise Ratio
    │       ├── audio_noise_snr_10/       # Audio with 10dB Signal-to-Noise Ratio
    │       ├── audio_noise_snr_20/       # Audio with 20dB Signal-to-Noise Ratio
    │       ├── qrels/                    # Query relevance judgments (TSV/JSONL)
    │       ├── corpus.jsonl              # Text corpus documents
    │       ├── queries.jsonl             # Original text queries
    │       ├── queries_with_audio_clean.jsonl         # Metadata mapping text to clean audio
    │       ├── queries_with_audio_noise_snr_0.jsonl   # Metadata for 0dB noise queries
    │       ├── queries_with_audio_noise_snr_10.jsonl  # Metadata for 10dB noise queries
    │       └── queries_with_audio_noise_snr_20.jsonl  # Metadata for 20dB noise queries
    └── zh/ (Chinese Datasets: DuRetrieval, MedicalRetrieval, T2Retrieval)
        └── [subset_name]/
            └── (Same structure as above)

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💾 How to Use the Dataset

You can download the dataset directly from this Hugging Face repository. To use the evaluation scripts, please refer to our GitHub Repository.