Research Tools & Code·arXiv cs.CL·4d ago

Scaling Conversational Hungarian ASR: The BEA-Dialogue+ Corpus

Researchers have expanded BEA-Dialogue, a Hungarian conversational speech recognition corpus, from 85 to 200 hours by relaxing speaker-overlap constraints while maintaining primary speaker separation. This work directly addresses a critical bottleneck in non-English ASR development: scarcity of naturalistic dialogue training data at scale. The controlled comparison between Whisper and FastConformer models across both dataset versions provides empirical guidance on the data-quality tradeoff that affects practitioners building speech systems for low-resource languages. For teams scaling multilingual ASR infrastructure, this establishes a replicable methodology for balancing dataset size against speaker generalization.

Modelwire context

Explainer

The paper's real contribution isn't just dataset expansion, but empirical evidence that speaker-overlap relaxation (a practical compromise) doesn't catastrophically degrade model generalization. This matters because most low-resource ASR work assumes you must choose between data quantity and speaker diversity, when the tradeoff may be more forgiving than previously believed.

This fits a clear pattern in recent coverage: non-English language communities are getting systematic infrastructure investments. The BenHalluEval framework for Bengali hallucination detection and the multilingual orthopedic decision-support work from late May both tackled reliability gaps in underserved languages by building reusable benchmarks. BEA-Dialogue+ follows the same logic for Hungarian ASR, establishing a methodology other low-resource language teams can replicate. The difference is domain: while those papers addressed LLM evaluation and clinical inference, this one targets the earlier pipeline stage where speech-to-text remains a bottleneck for any downstream NLP work in non-English contexts.

If teams working on other low-resource languages (Czech, Romanian, Tagalog) adopt this speaker-overlap relaxation method and report similar generalization curves, that confirms the finding generalizes beyond Hungarian. If they report different tradeoffs (steeper accuracy drops), that signals language-specific factors matter more than the methodology suggests.

Coverage we drew on

BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali · arXiv cs.CL

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MentionsBEA-Dialogue+ · Whisper · FastConformer · Hungarian

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