# Audio De-identification: A New Entity Recognition Task

arXiv: https://arxiv.org/abs/1903.07037 · HTML: https://mlsanity.com/paper/1903.07037/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2019.

## Built on by (most cited first, 16 of 16)

- [SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech](https://mlsanity.com/paper/2111.10367/) (2021)
- [SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks](https://mlsanity.com/paper/2212.10525/) (2022)
- [AISHELL-NER: Named Entity Recognition from Chinese Speech](https://mlsanity.com/paper/2202.08533/) (2022)
- [DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterances](https://mlsanity.com/paper/2110.07592/) (2021)
- [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)
- [Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech](https://mlsanity.com/paper/2105.04719/) (2021)
- [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)
- [Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities](https://mlsanity.com/paper/2502.00451/) (2025)
- [Medical Spoken Named Entity Recognition](https://mlsanity.com/paper/2406.13337/) (2024)
- [DialogPII: A multilingual dataset of synthetic dialog transcripts to detect personal information](https://mlsanity.com/paper/2606.30312/) (2026)
- [rx-anon -- A Novel Approach on the De-Identification of Heterogeneous Data based on a Modified Mondrian Algorithm](https://mlsanity.com/paper/2105.08842/) (2021)
- [Leveraging Cross-Lingual Transfer Learning in Spoken Named Entity Recognition Systems](https://mlsanity.com/paper/2307.01310/) (2023)
- [Token-Level Privacy in Large Language Models](https://mlsanity.com/paper/2503.03652/) (2025)
- [Is Word Error Rate Enough? Rethinking Privacy Evaluation in Speech with Entity-Aware Metrics](https://mlsanity.com/paper/2610.08831/) (2026)
- [AegisVox: Factorized Privacy and Clinical Preservation for Long-Form Speech](https://mlsanity.com/paper/2610.14643/) (2026)

## Abstract (arXiv)

Named Entity Recognition (NER) has been mostly studied in the context of written text. Specifically, NER is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor. In such recordings, audio spans with personal information should be redacted, similar to the redaction of sensitive character spans in de-ID for written text. The application of NER in the context of audio de-identification has yet to be fully investigated. To this end, we define the task of audio de-ID, in which audio spans with entity mentions should be detected. We then present our pipeline for this task, which involves Automatic Speech Recognition (ASR), NER on the transcript text, and text-to-audio alignment. Finally, we introduce a novel metric for audio de-ID and a new evaluation benchmark consisting of a large labeled segment of the Switchboard and Fisher audio datasets and detail our pipeline's results on it.