  While extensively explored in text-based tasks, Named Entity Recognition
(NER) remains largely neglected in spoken language understanding. Existing
resources are limited to a single, English-only dataset. This paper addresses
this gap by introducing MSNER, a freely available, multilingual speech corpus
annotated with named entities. It provides annotations to the VoxPopuli dataset
in four languages (Dutch, French, German, and Spanish). We have also releasing
an efficient annotation tool that leverages automatic pre-annotations for
faster manual refinement. This results in 590 and 15 hours of silver-annotated
speech for training and validation, alongside a 17-hour, manually-annotated
evaluation set. We further provide an analysis comparing silver and gold
annotations. Finally, we present baseline NER models to stimulate further
research on this newly available dataset.
