# Named Entity Recognition for Audio De-Identification

arXiv: https://arxiv.org/abs/2204.12622 · HTML: https://mlsanity.com/paper/2204.12622/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2022.

## Built on by (most cited first, 4 of 4)

- [SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks](https://mlsanity.com/paper/2212.10525/) (2022)
- [ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correction](https://mlsanity.com/paper/2310.05129/) (2023)
- [A Survey on Current Trends and Recent Advances in Text Anonymization](https://mlsanity.com/paper/2508.21587/) (2025)
- [DialogPII: A multilingual dataset of synthetic dialog transcripts to detect personal information](https://mlsanity.com/paper/2606.30312/) (2026)

## Abstract (arXiv)

Data anonymization is often a task carried out by humans. Automating it would reduce the cost and time required to complete this task. This paper presents a pipeline to automate the anonymization of audio data in French. We propose a pipeline, which takes audio files with their transcriptions and removes the named entities (NEs) present in the audio. Our pipeline is made up of a forced aligner, which aligns words in an audio transcript with speech and a model that performs named entity recognition (NER). Then, the audio segments that correspond to NEs are substituted with silence to anonymize audio. We compared forced aligners and NER models to find the best ones for our scenario. We evaluated our pipeline on a small hand-annotated dataset, achieving an F1 score of 0.769. This result shows that automating this task is feasible.