# SDS-200: A Swiss German Speech to Standard German Text Corpus

arXiv: https://arxiv.org/abs/2205.09501 · HTML: https://mlsanity.com/paper/2205.09501/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 19 May 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://mlsanity.com/paper/2010.05171/) (2020)
- [Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates](https://mlsanity.com/paper/1911.03167/) (2019)
- [SwissDial: Parallel Multidialectal Corpus of Spoken Swiss German](https://mlsanity.com/paper/2103.11401/) (2021)
- [Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech to Standard German Text Corpus](https://mlsanity.com/paper/2010.02810/) (2020)

## Built on by (most cited first, 22 of 22)

- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions](https://mlsanity.com/paper/2305.18855/) (2023)
- [A Survey of Corpora for Germanic Low-Resource Languages and Dialects](https://mlsanity.com/paper/2304.09805/) (2023)
- [Fine-tuning Whisper on Low-Resource Languages for Real-World Applications](https://mlsanity.com/paper/2412.15726/) (2024)
- [Spaiche: Extending State-of-the-Art ASR Models to Swiss German Dialects](https://mlsanity.com/paper/2304.11075/) (2023)
- [Does Whisper understand Swiss German? An automatic, qualitative, and human evaluation](https://mlsanity.com/paper/2404.19310/) (2024)
- [Swiss German Speech to Text system evaluation](https://mlsanity.com/paper/2207.00412/) (2022)
- [Dialect Transfer for Swiss German Speech Translation](https://mlsanity.com/paper/2310.09088/) (2023)
- [A Multi-Dialectal Dataset for German Dialect ASR and Dialect-to-Standard Speech Translation](https://mlsanity.com/paper/2506.02894/) (2025)
- [The ParlaSpeech Collection of Automatically Generated Speech and Text Datasets from Parliamentary Proceedings](https://mlsanity.com/paper/2409.15397/) (2024)
- [Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German Varieties](https://mlsanity.com/paper/2402.02078/) (2024)
- [HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation](https://mlsanity.com/paper/2306.11252/) (2023)
- [2nd Swiss German Speech to Standard German Text Shared Task at SwissText 2022](https://mlsanity.com/paper/2301.06790/) (2023)
- [Improving Metrics for Speech Translation](https://mlsanity.com/paper/2305.12918/) (2023)
- [Text-to-Speech Pipeline for Swiss German -- A comparison](https://mlsanity.com/paper/2305.19750/) (2023)
- [Advancing STT for Low-Resource Real-World Speech](https://mlsanity.com/paper/2506.08836/) (2025)
- [Subtitle-Aligned Fine-Tuning of Whisper for Swiss German ASR: Benchmark Contamination, Convention Mismatch, and an Honest Baseline at 25.6% WER (13.8% cWER)](https://mlsanity.com/paper/2606.07608/) (2026)
- [Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech](https://mlsanity.com/paper/2504.00780/) (2025)
- [Voice Adaptation for Swiss German](https://mlsanity.com/paper/2505.22054/) (2025)
- [Swiss Parliaments Corpus Re-Imagined (SPC_R): Enhanced Transcription with RAG-based Correction and Predicted BLEU](https://mlsanity.com/paper/2506.07726/) (2025)
- [SwissGPC v1.0 -- The Swiss German Podcasts Corpus](https://mlsanity.com/paper/2509.19866/) (2025)
- [FineWeb-CLaR: Culture, Language, and Region Annotations for Benchmark-Aligned Corpus Auditing](https://mlsanity.com/paper/2609.25298/) (2026)

## Abstract (arXiv)

We present SDS-200, a corpus of Swiss German dialectal speech with Standard German text translations, annotated with dialect, age, and gender information of the speakers. The dataset allows for training speech translation, dialect recognition, and speech synthesis systems, among others. The data was collected using a web recording tool that is open to the public. Each participant was given a text in Standard German and asked to translate it to their Swiss German dialect before recording it. To increase the corpus quality, recordings were validated by other participants. The data consists of 200 hours of speech by around 4000 different speakers and covers a large part of the Swiss-German dialect landscape. We release SDS-200 alongside a baseline speech translation model, which achieves a word error rate (WER) of 30.3 and a BLEU score of 53.1 on the SDS-200 test set. Furthermore, we use SDS-200 to fine-tune a pre-trained XLS-R model, achieving 21.6 WER and 64.0 BLEU.