|
|
|
|
| --- |
| 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. |
