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irlab-daiict/SqCLIRIL

🗣️ 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… See the full description on the dataset page: https://huggingface.co/datasets/irlab-daiict/SqCLIRIL.

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Dataset Card

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 prettyname: SqCLIRIL sizecategories:
—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.