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---
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: 17175733.0
num_examples: 172
download_size: 12084820
dataset_size: 17175733.0
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. |