Abstract
We introduce RO-N3WS, a benchmark Romanian speech dataset designed to improve generalization in automatic speech recognition (ASR), particularly in low-resource and out-of-distribution (OOD) conditions. RO-N3WS comprises over 126 hours of transcribed audio collected from broadcast news, literary audiobooks, film dialogue, children's stories, and conversational podcast speech. This diversity enables robust training and fine-tuning across stylistically distinct domains. We evaluate several state-of-the-art ASR systems (Whisper, Wav2Vec 2.0) in both zero-shot and fine-tuned settings, and conduct controlled comparisons using synthetic data generated with expressive TTS models. Our results show that even limited fine-tuning on real speech from RO-N3WS yields substantial WER improvements over zero-shot baselines. We will release all models, scripts, and data splits to support reproducible research in multilingual ASR, domain adaptation, and lightweight deployment.
Abstract (translated)
我们介绍了RO-N3WS，这是一个为改善自动语音识别（ASR）系统在低资源和分布外（OOD）条件下的泛化能力而设计的罗马尼亚语语音数据集。RO-N3WS包含超过126小时经过转录的音频资料，这些资料来自广播新闻、文学有声书、电影对话、儿童故事以及对话型播客演讲等不同来源。这种多样性使得在风格迥异的领域内进行稳健的训练和微调成为可能。 我们在零样本和微调两种设置下评估了几种最先进的ASR系统（如Whisper和Wav2Vec 2.0），并使用具有表现力的TTS模型生成的合成数据进行了控制比较。我们的结果显示，即使是在RO-N3WS的真实语音上进行有限度的微调也能显著改善相对于零样本基准条件下的错误词率（WER）。 为了支持多语言ASR、领域适应和轻量级部署方面的可重复研究工作，我们将发布所有模型、脚本及数据分割。
URL
https://arxiv.org/abs/2603.02368
