Datasets:
File size: 3,381 Bytes
d37be22 03d009d af89977 03d009d d37be22 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
---
license: cc-by-sa-4.0
task_categories:
- text-retrieval
- automatic-speech-recognition
language:
- en
- zh
tags:
- spoken-query-retrieval
- information-retrieval
- audio-text-retrieval
- mteb
- c-mteb
- robustness
pretty_name: SQuTR
size_categories:
- 10K<n<100K
---
# SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval
[](https://github.com/ttoyekk1a/SQuTR-Spoken-Query-to-Text-Retrieval)
[](https://arxiv.org/abs/2602.12783)
**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**.
---
## 🌟 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.
---
## 📂 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.
```text
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)
```
---
## 💾 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](https://github.com/ttoyekk1a/SQuTR-Spoken-Query-to-Text-Retrieval). |