Dialect identification is a critical task in speech processing and language technology, enhancing various applications such as speech recognition, speaker verification, and many others. While most research studies have been dedicated to dialect identification in widely spoken languages, limited attention has been given to dialect identification in low-resource languages, such as Romanian. To address this research gap, we introduce RoDia, the first dataset for Romanian dialect identification from speech. The RoDia dataset includes a varied compilation of speech samples from five distinct regions of Romania, covering both urban and rural environments, totaling 2 hours of manually annotated speech data. Along with our dataset, we introduce a set of competitive models to be used as baselines for future research. The top scoring model achieves a macro F1 score of 59.83% and a micro F1 score of 62.08%, indicating that the task is challenging. We thus believe that RoDia is a valuable resource that will stimulate research aiming to address the challenges of Romanian dialect identification. We publicly release our dataset and code at https://github.com/codrut2/RoDia.

翻译：方言识别是语音处理与语言技术中的关键任务，可提升语音识别、说话人验证等多种应用的效果。尽管多数研究致力于广泛使用语言的方言识别，但对罗马尼亚语等低资源语言的方言识别关注有限。为填补这一研究空白，我们提出RoDia——首个用于罗马尼亚语方言语音识别的数据集。该数据集包含来自罗马尼亚五个不同地区的多样化语音样本，覆盖城乡环境，共计2小时人工标注的语音数据。除数据集外，我们还提供一组作为未来研究基准的竞争性模型。最优模型的宏F1分数为59.83%，微F1分数为62.08%，表明该任务具有挑战性。因此，我们认为RoDia是推动罗马尼亚语方言识别挑战研究的宝贵资源。我们已在https://github.com/codrut2/RoDia公开数据集与代码。