---
language:
- hi
- gu
- bn
- kn
- en
tags:
- speech
- text
- asr
- multilingual
- indian-languages
- cross-lingual-ir
- spoken-query
task_categories:
- automatic-speech-recognition
- text-retrieval
license: cc-by-4.0
pretty_name: SqCLIRIL
size_categories:
- n<1K
---



# 🗣️ SqCLIRIL: Spoken Query Benchmark for Cross-Lingual IR in Indian Languages

**SqCLIRIL** is a **Spoken Query Benchmark** designed to evaluate **cross-lingual information retrieval (CLIR)** systems using both **spoken** and **text** queries.  
It covers **five Indian languages** — **Hindi**, **Gujarati**, **Bengali**, **Kannada**, and **English** — with diverse speech samples from **male** and **female** speakers to capture natural variability in pronunciation and acoustic conditions.

---

## 📘 Dataset Summary

| Feature           | Description                                                                 |
|-------------------|-----------------------------------------------------------------------------|
| **Dataset name**  | SqCLIRIL                                                                     |
| **Languages**     | Hindi (`hi`), Gujarati (`gu`), Bengali (`bn`), Kannada (`kn`), English (`en`) |
| **Modalities**    | Text, Speech (WAV), ASR Transcriptions                                       |
| **Speakers**      | Male and Female                                                             |
| **Data Type**     | Queries and their spoken utterances                                          |
| **Format**        | `.tsv` (queries and transcriptions), `.wav` (spoken queries)                |

---

## 🧩 Dataset Structure

The dataset is organized into three main folders:

```
SqCLIRIL/
│
├── text/
│   ├── hindi/
│   │   ├── trec_dl19_hindi_query.tsv
│   │   ├── trec_dl20_hindi_query.tsv
│   │   └── trec_dl1920_hindi_query.tsv
│   ├── gujarati/
│   ├── bengali/
│   ├── kannada/
│   └── english/
│
├── asr/
│   ├── hindi/
│   │   ├── male/
│   │   │   ├── m1/ → sq_hi_m1.tsv
│   │   │   ├── m2/ → sq_hi_m2.tsv
│   │   └── female/
│   │       ├── f1/ → sq_hi_f1.tsv
│   │       ├── f2/ → sq_hi_f2.tsv
│   ├── ...
│
└── speech/
    ├── hindi/
    │   ├── male/
    │   │   ├── m1/ → 123.wav, 124.wav, ...
    │   │   ├── m2/ → ...
    │   └── female/
    │       ├── f1/ → 201.wav, ...
    │       ├── f2/ → ...
    ├── ...
```

### Folder Descriptions

- **`text/`**: Contains text queries for each language in three benchmark splits (`trec_dl1920`, `trec_dl19`, `trec_dl20`).
- **`speech/`**: Contains recorded spoken queries (WAV files) from both male and female speakers.
- **`asr/`**: Contains automatic speech recognition (ASR) transcriptions of the spoken queries, structured by gender and speaker ID.

---

## 🗂️ Example Structure (Hindi)

```
SqCLIRIL/
├── text/hindi/trec_dl19_hindi_query.tsv
├── speech/hindi/male/m1/123.wav
├── speech/hindi/male/m1/124.wav
├── speech/hindi/female/f1/201.wav
├── asr/hindi/male/m1/sq_hi_m1.tsv
├── asr/hindi/female/f1/sq_hi_f1.tsv
```

Each line in the `sq_hi_f1.tsv` corresponds to the transcription of the spoken file with the same query ID (e.g., `123.wav`).

---

## 💡 Intended Uses

- **Cross-lingual information retrieval (CLIR)**
- **Speech-to-text retrieval**
- **Multilingual query understanding**
- **Spoken Query Search in Indian Languages**

---

## ⚙️ Data Fields

| Field             | Description                                           |
|-------------------|-------------------------------------------------------|
| `query_id`        | Unique identifier for the query (e.g., `123`)         |
| `language`        | One of {`hi`, `gu`, `bn`, `kn`, `en`}                 |
| `text_query`      | Original text form of the query                       |
| `speech_audio`    | Path to the `.wav` file containing the spoken version |
| `asr_transcription` | Automatic transcription of the spoken query         |
| `speaker_id`      | Speaker identity (e.g., `m1`, `f2`)                   |
| `gender`          | Male/Female                                           |

---

## 📊 Data Splits

Each language contains three splits:

| Split             | Description                              |
|-------------------|------------------------------------------|
| `trec_dl19`       | TREC Deep Learning Track 2019 queries    |
| `trec_dl20`       | TREC Deep Learning Track 2020 queries    |
| `trec_dl1920`     | Combined 2019–2020 queries               |

---

## 🎧 Audio Details

| Property    | Value     |
|-------------|-----------|
| Format      | WAV       |
| Sampling Rate | 16 kHz |
| Channels    | Mono      |
| Environment | Natural home and lab settings |

---

## 📜 Citation

If you use this dataset, please cite:

```bibtex
@article{DAVE2025,
  title = {SqCLIRIL: Spoken query cross-lingual information retrieval in Indian languages},
  journal = {Pattern Recognition Letters},
  year = {2025},
  issn = {0167-8655},
  doi = {https://doi.org/10.1016/j.patrec.2025.08.022},
  url = {https://www.sciencedirect.com/science/article/pii/S0167865525003071},
  author = {Bhargav Dave and Prasenjit Majumder},
}
```

---

## ⚖️ License

> **License:** CC BY 4.0

---

## 🙌 Acknowledgements

We thank all contributors and speakers involved in building this multilingual benchmark for advancing speech-based cross-lingual IR research in India.