# MSNER: A Multilingual Speech Dataset for Named Entity Recognition

arXiv: https://arxiv.org/abs/2405.11519 · HTML: https://mlsanity.com/paper/2405.11519/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 19 May 2024.

## Builds on (most cited first, 11 of 11)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [SLURP: A Spoken Language Understanding Resource Package](https://mlsanity.com/paper/2011.13205/) (2020)
- [Speech Model Pre-training for End-to-End Spoken Language Understanding](https://mlsanity.com/paper/1904.03670/) (2019)
- [SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech](https://mlsanity.com/paper/2111.10367/) (2021)
- [T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition](https://mlsanity.com/paper/2209.12616/) (2022)
- [Spoken Language Understanding on the Edge](https://mlsanity.com/paper/1810.12735/) (2018)
- [Mitigating Transformer Overconfidence via Lipschitz Regularization](https://mlsanity.com/paper/2306.06849/) (2023)
- [Capsule Networks for Low Resource Spoken Language Understanding](https://mlsanity.com/paper/1805.02922/) (2018)
- [Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers](https://mlsanity.com/paper/2104.01604/) (2021)

## Built on by (most cited first, 2 of 2)

- [SENSE models: an open source solution for multilingual and multimodal semantic-based tasks](https://mlsanity.com/paper/2509.12093/) (2025)
- ["I've Heard of You!": Generate Spoken Named Entity Recognition Data for Unseen Entities](https://mlsanity.com/paper/2412.19102/) (2024)

## Abstract (arXiv)

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.