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New Hungarian ASR Corpus Doubles Training Data, Improves Accuracy

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced BEA-Dialogue+, an expanded corpus for Hungarian conversational automatic speech recognition (ASR). This new dataset increases the available training data to 200 hours, relaxing split criteria to allow for more material while maintaining speaker separation. Evaluations using Whisper and FastConformer models demonstrate that the larger dataset, especially when combined with Serialized Output Training (SOT) fine-tuning, leads to significant improvements in transcription accuracy metrics. AI

IMPACT Provides a larger, more challenging benchmark for Hungarian dialogue ASR, enabling better training and evaluation of transcription systems.

RANK_REASON The cluster contains an academic paper detailing a new dataset and evaluation of ASR models.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Hungarian ASR Corpus Doubles Training Data, Improves Accuracy

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The cluster contains an academic paper detailing a new dataset and evaluation of ASR models.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · M\'at\'e Gedeon, Piroska Zs\'ofia Barta, P\'eter Mihajlik, Katalin M\'ady ·

    Scaling Conversational Hungarian ASR: The BEA-Dialogue+ Corpus

    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…

  2. arXiv cs.AI TIER_1 English(EN) · Katalin Mády ·

    Scaling Conversational Hungarian ASR: The BEA-Dialogue+ Corpus

    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 onl…

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