# End-to-end named entity extraction from speech

arXiv: https://arxiv.org/abs/1805.12045 · HTML: https://mlsanity.com/paper/1805.12045/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2018.

## Built on by (most cited first, 11 of 11)

- [Is "moby dick" a Whale or a Bird? Named Entities and Terminology in Speech Translation](https://mlsanity.com/paper/2109.07439/) (2021)
- [Who Are We Talking About? Handling Person Names in Speech Translation](https://mlsanity.com/paper/2205.06755/) (2022)
- [Performance Analysis of Speech Encoders for Low-Resource SLU and ASR in Tunisian Dialect](https://mlsanity.com/paper/2407.04533/) (2024)
- [Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech](https://mlsanity.com/paper/2105.04719/) (2021)
- [SENSE models: an open source solution for multilingual and multimodal semantic-based tasks](https://mlsanity.com/paper/2509.12093/) (2025)
- [Representation based meta-learning for few-shot spoken intent recognition](https://mlsanity.com/paper/2106.15238/) (2021)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource setting](https://mlsanity.com/paper/2207.08179/) (2022)
- [Trustera: A Live Conversation Redaction System](https://mlsanity.com/paper/2303.09438/) (2023)
- [TokenVerse: Towards Unifying Speech and NLP Tasks via Transducer-based ASR](https://mlsanity.com/paper/2407.04444/) (2024)
- ["Sorry, I Didn't Catch That": How Speech Models Miss What Matters Most](https://mlsanity.com/paper/2602.12249/) (2026)

## Abstract (arXiv)

Named entity recognition (NER) is among SLU tasks that usually extract semantic information from textual documents. Until now, NER from speech is made through a pipeline process that consists in processing first an automatic speech recognition (ASR) on the audio and then processing a NER on the ASR outputs. Such approach has some disadvantages (error propagation, metric to tune ASR systems sub-optimal in regards to the final task, reduced space search at the ASR output level...) and it is known that more integrated approaches outperform sequential ones, when they can be applied. In this paper, we present a first study of end-to-end approach that directly extracts named entities from speech, though a unique neural architecture. On a such way, a joint optimization is able for both ASR and NER. Experiments are carried on French data easily accessible, composed of data distributed in several evaluation campaign. Experimental results show that this end-to-end approach provides better results (F-measure=0.69 on test data) than a classical pipeline approach to detect named entity categories (F-measure=0.65).