Update README.md

README.md

@@ -35,6 +35,69 @@ While speech interaction is becoming a primary interface for IR systems, perform
 * **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.
 
+---
+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
+
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/ttoyekk1a/SQuTR-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**.
+
+---
+
+## 🌟 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