# Scaling Conversational Hungarian ASR: The BEA-Dialogue+ Corpus

arXiv: https://arxiv.org/abs/2605.31469 · HTML: https://mlsanity.com/paper/2605.31469/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 29 May 2026.

## Builds on (most cited first, 6 of 6)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://mlsanity.com/paper/2305.05084/) (2023)
- [Serialized Output Training for End-to-End Overlapped Speech Recognition](https://mlsanity.com/paper/2003.12687/) (2020)
- [Deep Neural Networks for Automatic Speech Processing: A Survey from Large Corpora to Limited Data](https://mlsanity.com/paper/2003.04241/) (2020)
- [BEA-Base: A Benchmark for ASR of Spontaneous Hungarian](https://mlsanity.com/paper/2202.00601/) (2022)

## Built on by (most cited first, 1 of 1)

- [Efficient ASR Training with Conversations that Never Happened](https://mlsanity.com/paper/2606.03957/) (2026)

## Abstract (arXiv)

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 natural conversations and enables a controlled study of the trade-off between additional training data and speaker overlap across the splits. We evaluate several Whisper- and FastConformer-based models on both corpus versions, including Serialized Output Training (SOT)-based fine-tuning for dialogue transcription. Our results show that the larger corpus is more challenging for models without fine-tuning, whereas SOT-based adaptation yields consistent improvements in WER, CER, cpWER, and cpCER. Overall, BEA-Dialogue+ provides a substantially larger yet still demanding benchmark for Hungarian dialogue ASR, and a practical resource for training and evaluating dialogue transcription systems.