dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: named_entities
sequence: string
- name: entity_types
sequence: string
- name: entity_languages
sequence: string
splits:
- name: test
num_bytes: 17175733
num_examples: 172
download_size: 12084820
dataset_size: 17175733
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
license: cc-by-4.0
language:
- de
size_categories:
- n<1K
SwissNER-Spoken is a curated collection of 173 short, spoken-style German sentences designed to evaluate Named-Entity Recognition (NER) and Automatic Speech Recognition (ASR) systems on Swiss-specific proper nouns.
Key features
• Focus on Switzerland – Every sentence contains up to three named entities that appear in everyday Swiss contexts: cities, villages, cantons, companies, mountains, lakes, rivers, landmarks, organizations, events and well-known personalities.
• Balanced regional coverage – All 26 Swiss cantons are represented, with entities drawn from German-, French-, Italian- and Romansh-speaking areas.
• Multilingual entity labels – For each entity, the dataset records the language of the
name (de, fr, it, rm or en) so models can test language-aware
recognition.
• Compact schema – Four CSV columns:
1. text – the sentence in German.
2. named_entities – comma-separated list of the entities in the sentence.
3. entity_types – aligned list of coarse entity classes (e.g. City, Company,
Mountain).
4. entity_languages – aligned list of language tags for each entity name.
• ASR-oriented style – Sentences are intentionally short, natural, and pronunciation- friendly, making the corpus ideal for measuring how well speech or text models handle Swiss proper nouns in real-world utterances.
Typical row
"Nestlé hat seinen Sitz in Vevey im Kanton Waadt.", "Nestlé, Vevey, Waadt", "Company, City, Canton", "fr, fr, de"
Use cases
- Benchmarking NER models on Swiss entities
- Stress-testing ASR/voice pipelines for pronunciation and transcription accuracy
- Data augmentation or few-shot prompts for multilingual Swiss NLP tasks
- Educational demos for Swiss geography, culture and corporate landscape
License
CC-BY-4.0 – attribution required; no additional restrictions.