  Recent advancements in Named Entity Recognition (NER) have significantly
improved the identification of entities in textual data. However, spoken NER, a
specialized field of spoken document retrieval, lags behind due to its limited
research and scarce datasets. Moreover, cross-lingual transfer learning in
spoken NER has remained unexplored. This paper utilizes transfer learning
across Dutch, English, and German using pipeline and End-to-End (E2E) schemes.
We employ Wav2Vec2-XLS-R models on custom pseudo-annotated datasets and
investigate several architectures for the adaptability of cross-lingual
systems. Our results demonstrate that End-to-End spoken NER outperforms
pipeline-based alternatives over our limited annotations. Notably, transfer
learning from German to Dutch surpasses the Dutch E2E system by 7% and the
Dutch pipeline system by 4%. This study not only underscores the feasibility of
transfer learning in spoken NER but also sets promising outcomes for future
evaluations, hinting at the need for comprehensive data collection to augment
the results.
