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Saar-Voice

A multi-speaker speech corpus for the Rhine Franconian dialect of German as spoken in Saarbrücken and the surrounding region, referred to loosely as Saarländisch.

Overview

Property Value
Speakers 9 (P01–P09)
Total duration ~6 hours
Sentences (recorded) 4,871
Sentences (unrecorded) 3,901
Sampling rate 22,050 Hz
Language Saarländisch (Rhine Franconian)
Format WAV (stereo, 16-bit)

Splits

Split Sentences Purpose
train 2,373 Model training
test 1,510 Test set
validation 268 Validation set
held_out 720 Held-out speaker evaluation

The held_out split contains recordings from speakers not seen during training, intended for further tests such as transfer learning experiments.

The dataset additionally includes unrecorded.json, a list of 3,905 sentences for which no audio was recorded, provided for completeness and for future work.

Speakers

The corpus includes 9 speakers (P01–P09) of varying age and gender, all native speakers of Saarländisch. Speaker metadata (age, gender) is not included in this release.

Text

Sentences were sourced from:

  • the MASSIVE dataset (German subset) + localized into Saarländisch dialect orthography
  • several books, poem and short story collections
  • texts provided directly by the local author community

A detailed breakdown is available in the corresponding paper.

Usage

from datasets import load_dataset

ds = load_dataset("UdS-LSV/Saar-Voice")

# Access the training split
for example in ds["train"]:
    audio = example["audio"]          # dict with array, sampling_rate, path
    text  = example["text"]           # dialect transcription
    spk   = example["speaker_id"]     # e.g. "P01"

Citation

If you use this dataset, please cite:

@misc{oberkircher2026saarvoice,
  title  = {Saar-Voice: A Multi-Speaker Saarbrücken Dialect Speech Corpus},
  author = {Lena S. Oberkircher and Jesujoba O. Alabi and Dietrich Klakow and Jürgen Trouvain},
  year   = {2026},
  url    = {https://arxiv.org/abs/2604.11803}
}

License

This dataset is released under CC BY 4.0.

Acknowledgements

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Paper for UdS-LSV/Saar-Voice