arXiv:2605.31469v1 Announce Type: cross Abstract: Conversational automatic speech recognition in Hungarian is constrained by the limited amount of publicly available dialogue-style training data. The BEA-Dialogue corpus addresses this need, but its strictly speaker-disjoint train/dev/eval split reduces the usable material to only 85 hours. In this paper, we introduce BEA-Dialogue+, an expanded version of the corpus that relaxes the split criterion for experimenters and dialogue partners while preserving complete separation of the primary speakers. This results in 200 hours of transcribed natur
The continuous drive to improve AI model performance necessitates larger and more diverse datasets, especially for less-resourced languages like Hungarian.
Improved conversational ASR for Hungarian enhances the accessibility and utility of AI in a specific linguistic context, paving the way for broader adoption and better services.
The availability of a significantly larger dataset (200 hours) for Hungarian ASR will lead to more robust and accurate speech recognition models for the language.
- · Hungarian AI developers
- · Companies offering AI services in Hungary
- · Hungarian linguistic research
- · Users of Hungarian voice interfaces
- · Developers relying solely on older, smaller datasets
Hungarian conversational AI applications will experience a noticeable improvement in accuracy and performance.
This improvement could spur further investment and development in Hungarian-specific AI solutions and services.
Enhanced linguistic AI capabilities might reduce digital language barriers and foster more seamless human-AI interaction in Hungarian-speaking regions.
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Read at arXiv cs.AI