Work

Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects

Year: 2026

Type: conference-paper

Abstract: Research on cross-dialectal transfer from a standard to a non-standard dialect variety has typically focused on text data.However, dialects are primarily spoken, and non-standard spellings cause issues in text processing.We compare standard-to-dialect transfer in three settings: text models, speech models, and cascaded systems where speech first gets automatically transcribed and then further processed by a text model.We focus on German dialects in the context of written and spoken intent classification -releasing the first dialectal audio intent classification dataset -with supporting experiments on topic classification.The speech-only setup provides the best results on the dialect data while the text-only setup works best on the standard data.While the cascaded systems lag behind the text-only models for German, they perform relatively well on the dialectal data if the transcription system generates normalized, standard-like output.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/2026.acl-long.309

Authors: Verena Blaschke, Miriam Winkler, Barbara Plank

Institutions: Munich Center for Machine Learning, Ludwig-Maximilians-Universität München

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: HORIZON EUROPE European Research Council

Award: Natural Language Understanding for non-standard languages and dialects