# Revisiting Speech Content Privacy

arXiv: https://arxiv.org/abs/2110.06760 · HTML: https://mlsanity.com/paper/2110.06760/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2021.

## Built on by (most cited first, 5 of 5)

- [The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization](https://mlsanity.com/paper/2601.11846/) (2026)
- [Exploratory Evaluation of Speech Content Masking](https://mlsanity.com/paper/2401.03936/) (2024)
- [Content Anonymization for Privacy in Long-form Audio](https://mlsanity.com/paper/2510.12780/) (2025)
- [Adversarial Representation Learning for Robust Privacy Preservation in Audio](https://mlsanity.com/paper/2305.00011/) (2023)
- [Is Word Error Rate Enough? Rethinking Privacy Evaluation in Speech with Entity-Aware Metrics](https://mlsanity.com/paper/2610.08831/) (2026)

## Abstract (arXiv)

In this paper, we discuss an important aspect of speech privacy: protecting spoken content. New capabilities from the field of machine learning provide a unique and timely opportunity to revisit speech content protection. There are many different applications of content privacy, even though this area has been under-explored in speech technology research. This paper presents several scenarios that indicate a need for speech content privacy even as the specific techniques to achieve content privacy may necessarily vary. Our discussion includes several different types of content privacy including recoverable and non-recoverable content. Finally, we introduce evaluation strategies as well as describe some of the difficulties that may be encountered.