扩展匈牙利语对话ASR：BEA-Dialogue+语料库
Scaling Conversational Hungarian ASR: The BEA-Dialogue+ Corpus
AI总结：
针对匈牙利语对话语音识别训练数据不足的问题，本文通过放宽分割标准扩展BEA-Dialogue语料库至200小时，并评估基于Whisper和FastConformer的模型，证明基于序列化输出训练的微调能持续改善识别性能。
摘要：
匈牙利语对话自动语音识别受到公开对话式训练数据有限的制约。BEA-Dialogue语料库解决了这一需求，但其严格的说话人分离的训练/开发/测试分割将可用材料减少到仅85小时。在本文中，我们介绍了BEA-Dialogue+，这是该语料库的扩展版本，它放宽了实验者和对话伙伴的分割标准，同时保持主要说话人的完全分离。这产生了200小时转录的自然对话，并允许对额外训练数据与分割间说话人重叠之间的权衡进行受控研究。我们在两个语料库版本上评估了多个基于Whisper和FastConformer的模型，包括基于序列化输出训练（SOT）的对话转录微调。我们的结果表明，对于未经微调的模型，较大的语料库更具挑战性，而基于SOT的适应在WER、CER、cpWER和cpCER上产生了一致的改进。总体而言，BEA-Dialogue+为匈牙利语对话ASR提供了一个更大但仍具挑战性的基准，以及用于训练和评估对话转录系统的实用资源。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
