---
title: "Standard-to-Dialect Transfer Trends Differ across Text and"
description: "For this, they release xSID-audio, the first dialectal spoken intent dataset, with Standard German and… 73 references, cited by 3 papers here."
authors: ["Verena Blaschke", "Miriam Winkler", "Barbara Plank"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.309/"
doi: "10.18653/v1/2026.acl-long.309"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/blaschke-2026-standard-dialect-transfer-trends-differ/"
---
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# Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects

**Authors:** [Verena Blaschke](https://md.research.elamur.ai/free/en/authors/verena-blaschke/), [Miriam Winkler](https://md.research.elamur.ai/free/en/authors/miriam-winkler/) and [Barbara Plank](https://md.research.elamur.ai/free/en/authors/barbara-plank/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.309/) · [PDF](https://aclanthology.org/2026.acl-long.309.pdf) · [DOI 10.18653/v1/2026.acl-long.309](https://doi.org/10.18653/v1/2026.acl-long.309) · [OpenAlex](https://openalex.org/W4416399816)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/blaschke-2026-standard-dialect-transfer-trends-differ/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks whether the usual approach of training on a standard language and testing on a closely related dialect behaves the same way for text and for speech. Dialects are mostly spoken, and their irregular spellings tend to break subword tokenizers. The authors compare three pipelines:
- **Text-only:** a text model trained on Standard German text.
- **Speech-only:** a speech encoder trained on German audio.
- **Cascaded:** ASR output passed to a text model trained on gold text.

The main task is intent classification for virtual-assistant queries, using MASSIVE/Speech-MASSIVE and xSID. For this, they release xSID-audio, the first dialectal spoken intent dataset, with Standard German and Bavarian read by the same speaker. A supporting topic-classification experiment uses German and eight Swiss German dialects from SwissDial.

The ranking of setups flips between standard and dialect data:
- On German, text-only is strongest.
- On the dialects, speech-only clearly wins.
- Cascaded systems trail on German but do fairly well on dialects when the ASR step effectively rewrites dialect speech into standard-like text.

The broader point is that conclusions drawn on standard languages may not carry over to dialects.

## Key points
- **New resource:** xSID-audio contains German and Bavarian recordings that match in content, speaker and recording conditions. The speaker is a native speaker of both varieties who also translated the Bavarian text. The full release has 300 dev and 500 test items per variety; 412 test items per variety are used after label mapping.
- **Intent data:** (Speech-)MASSIVE German has 2.5k/459/361 train/dev/test instances, mapped onto xSID's 10-intent label set. There is a Bavarian MASSIVE test subset of 361 items, and all text is lowercased to match MASSIVE.
- **Topic data:** SwissDial provides German with 1.5k/194/396 instances, plus 8 Swiss German dialects with 396 test items each.
- **Models:** encoders only (e.g., mBERT, XLM-R, mDeBERTa, mHuBERT, XLS-R 300M, Whisper). The experiments cover nine ASR models and four text models, each run with three seeds.
- **Robustness check:** a subset of cascaded systems was also trained on ASR transcripts instead of gold text. Absolute scores shifted, but the transfer trends stayed the same.
- **Interpretation:** the speech models perform well on Bavarian classification even though they transcribe Bavarian poorly. This suggests they saw little Bavarian data in pre-training, yet still transfer to it.

## Interesting ideas
- Hold the content fixed and vary only modality and variety, using parallel text/audio and standard/dialect data. Any performance gap can then be traced to the input representation rather than to the data.
- A front-end that silently normalizes dialectal input toward standard forms can rescue a downstream model trained only on the standard variety.
- Use a single bilingual speaker for both varieties so that speaker and recording effects cancel out, a cheap way to remove confounds from a small evaluation set.
- Train on gold input to keep computation manageable, then check on a small subset that training on noisy input leaves the conclusions unchanged.
- Map labels from different datasets onto one shared inventory and drop anything that does not fit, so that results across corpora stay comparable.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): build evaluation sets with parallel standard and non-standard versions of the same sentences, so drops in parsing or frame accuracy can be attributed to surface variation rather than content.
- **I2** (tokens): add an explicit, logged normalization layer that maps non-standard or dialectal English spellings to standard forms before tagging, keeping the original token for explainability.
- **I3** (learn): when creating variant test data such as dialect rewrites, have one source produce every version, so differences reflect only the variety being tested.
- **I4** (learn): train witness parsers on gold UD trees, and on a sample confirm that training on silver or noisy input gives the same relative rankings before trusting the cheaper setup.
- **I5** (frames): align speech-act or intent labels from different sources to a single frame inventory, discarding unmappable items, before comparing performance across corpora.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Ludwig-Maximilians-Universität München](https://md.research.elamur.ai/free/en/universities/ludwig-maximilians-universitat-munchen/) — University, Munich, Germany · 107 works · 56,478 citations
- [Munich Center for Machine Learning](https://md.research.elamur.ai/free/en/universities/munich-center-for-machine-learning/) — University, Munich, Germany · 57 works · 1,094 citations

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🧭 Topics (1): [🎙️ Speech & spoken language](https://md.research.elamur.ai/free/en/topics/speech/) · 480 works

## Cite

Cite as: Verena Blaschke, Miriam Winkler and Barbara Plank. 2026. Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{blaschke2026standard,
  title = {{Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects}},
  author = {Verena Blaschke, Miriam Winkler and Barbara Plank},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.309},
  url = {https://doi.org/10.18653/v1/2026.acl-long.309}
}
```

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## References

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*Updated 1 November 2026*