aranemini/southern-kurdish-asr

Bestun: Southern Kurdish speech recognition resources and benchmarking

This repository provides speech recognition (ASR) resources for Southern Kurdish (ISO 639-3: `sdh`), a threatened variant of the Kurdish macrolanguage. It includes:

• —Bestun training corpus: ~30 hours of manually validated read speech
• —Evaluation benchmark: 773 validated utterances (86.74 minutes) recorded by 8 speakers from different Southern Kurdish vernacular regions

The dataset and models are released under CC BY-NC-ND 4.0.

Contents and splits

The dataset is organized into two main splits:

• —train: Bestun training corpus (crowdsourced read speech)
• —test: Southern Kurdish evaluation benchmark (100 sentences × multiple speakers; validated subset)

Recording environments (training)

Training data were collected with two crowdsourcing tools:

• —telegram_bot: recorded via a Telegram bot
• —web_tool: recorded via a web-based recording tool jiridastkrd.com

Data statistics (from the paper)

Training corpus (Bestun)

• —Audio format: mono, 24 kHz, WAV
• —Total recordings: 18,636
• —Unique utterances: 15,273
• —Duration: ~30 hours
• —Speakers: 208 (age range 18–49)

Evaluation benchmark

• —Speakers: 8 (from different vernacular regions)
• —Validated utterances: 773
• —Duration: 86.74 minutes

Files and columns

Each example contains an audio field and metadata. Typical columns:

• —audio: speech waveform (WAV)
• —kurdish: transcription in Southern Kurdish Arabic-based script
• —speaker_id: anonymized speaker identifier
• —gender: speaker gender when available (M / F)
• —region: vernacular / region label when available
• —subset: optional source label used during dataset construction

How to download / use

Option 1 — Use datasets in Python

from datasets import load_dataset

ds = load_dataset("aranemini/southern-kurdish-asr")
print(ds)
print(ds["train"][0])

Citation

If you use this dataset, please cite our paper:

@inproceedings{mohammadamini2026southernkurdish,
  title     = {Southern Kurdish speech recognition resources and benchmarking},
  author    = {Mohammad Mohammadamini and Marie Tahon},
  booktitle = {Proceedings of the 2026 Language Resources and Evaluation Conference (LREC 2026)},
  year      = {2026},
  address   = {Palma, Mallorca (Spain)},
  publisher = {European Language Resources Association (ELRA)},
}