# Dialect Transfer for Swiss German Speech Translation

arXiv: https://arxiv.org/abs/2310.09088 · HTML: https://mlsanity.com/paper/2310.09088/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (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)
- [SwissDial: Parallel Multidialectal Corpus of Spoken Swiss German](https://mlsanity.com/paper/2103.11401/) (2021)
- [SDS-200: A Swiss German Speech to Standard German Text Corpus](https://mlsanity.com/paper/2205.09501/) (2022)
- [STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions](https://mlsanity.com/paper/2305.18855/) (2023)
- [Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech to Standard German Text Corpus](https://mlsanity.com/paper/2010.02810/) (2020)
- [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)

## Built on by (most cited first, 7 of 7)

- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [Fine-tuning Whisper on Low-Resource Languages for Real-World Applications](https://mlsanity.com/paper/2412.15726/) (2024)
- [Isolating Culture Neurons in Multilingual Large Language Models](https://mlsanity.com/paper/2508.02241/) (2025)
- [Advancing STT for Low-Resource Real-World Speech](https://mlsanity.com/paper/2506.08836/) (2025)
- [Voice Adaptation for Swiss German](https://mlsanity.com/paper/2505.22054/) (2025)
- [SwissGPC v1.0 -- The Swiss German Podcasts Corpus](https://mlsanity.com/paper/2509.19866/) (2025)
- [Evaluating Japanese Dialect Robustness Across Speech and Text-based Large Language Models](https://mlsanity.com/paper/2606.25436/) (2026)

## Abstract (arXiv)

This paper investigates the challenges in building Swiss German speech translation systems, specifically focusing on the impact of dialect diversity and differences between Swiss German and Standard German. Swiss German is a spoken language with no formal writing system, it comprises many diverse dialects and is a low-resource language with only around 5 million speakers. The study is guided by two key research questions: how does the inclusion and exclusion of dialects during the training of speech translation models for Swiss German impact the performance on specific dialects, and how do the differences between Swiss German and Standard German impact the performance of the systems? We show that dialect diversity and linguistic differences pose significant challenges to Swiss German speech translation, which is in line with linguistic hypotheses derived from empirical investigations.