CV-18 NER: Augmented Common Voice for Named Entity Recognition from Arabic Speech
This work addresses the lack of resources and benchmarks for Arabic speech NER, providing a foundational dataset and models for the research community, though it is incremental in applying existing methods to a new language domain.
The paper tackles the problem of end-to-end named entity recognition (NER) from Arabic speech, which is under-explored due to linguistic complexity and limited data, by introducing CV-18 NER, the first publicly available dataset for this task, and shows that end-to-end models outperform pipeline systems, achieving up to 38.0% CVER on the test set.
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.