End-to-end speech Named Entity Recognition (NER) aims to directly extract entities from speech. Prior work has shown that end-to-end (E2E) approaches can outperform cascaded pipelines for English, French, and Chinese, but Arabic remains under-explored due to its morphological complexity, the absence of short vowels, and limited annotated resources. We introduce CV-18 NER, the first publicly available dataset for NER from Arabic speech, created by augmenting the Arabic Common Voice 18 corpus with manual NER annotations following the fine-grained Wojood schema (21 entity types). We benchmark both pipeline systems (ASR + text NER) and E2E models based on Whisper and AraBEST-RQ. E2E systems substantially outperform the best pipeline configuration on the test set, reaching 37.0% CoER (AraBEST-RQ 300M) and 38.0% CVER (Whisper-medium). Further analysis shows that Arabic-specific self-supervised pretraining yields strong ASR performance, while multilingual weak supervision transfers more effectively to joint speech-to-entity learning, and that larger models may be harder to adapt in this low-resource setting. Our dataset and models are publicly released, providing the first open benchmark for end-to-end named entity recognition from Arabic speech https://huggingface.co/datasets/Elyadata/CV18-NER.

翻译：端到端语音命名实体识别（NER）旨在从语音中直接提取实体。先前研究表明，端到端方法在英语、法语和中文任务中优于级联流水线，但由于阿拉伯语形态复杂、缺乏短元音以及标注资源有限，该语言仍未被充分探索。我们提出CV-18 NER，这是首个公开的阿拉伯语音NER数据集，通过采用细粒度Wojood标注模式（21种实体类型）对阿拉伯语Common Voice 18语料库进行手工NER标注而构建。我们分别对基于Whisper和AraBEST-RQ的流水线系统（ASR+文本NER）与端到端模型进行了基准测试。端到端系统在测试集上显著优于最佳流水线配置，分别达到37.0% CoER（AraBEST-RQ 300M）和38.0% CVER（Whisper-medium）。进一步分析表明，阿拉伯语专用自监督预训练能实现优异ASR性能，而多语言弱监督在联合语音到实体学习任务中的迁移效果更佳；同时，在此低资源场景下，更大规模模型可能更难适应。本数据集与模型已公开发布，为阿拉伯语音端到端命名实体识别提供了首个开放基准：https://huggingface.co/datasets/Elyadata/CV18-NER