Revisiting Speech Content Privacy
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Oct 2021.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 2.
5 papers.
- 2026The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymizationcited by 8
- 2024Exploratory Evaluation of Speech Content Maskingcited by 3
- 2025Content Anonymization for Privacy in Long-form Audiocited by 3
- 2023Adversarial Representation Learning for Robust Privacy Preservation in Audiocited by 2
- 2026Is Word Error Rate Enough? Rethinking Privacy Evaluation in Speech with Entity-Aware Metricscited by 0
- 2026Is Word Error Rate Enough? Rethinking Privacy Evaluation in Speech with Entity-Aware Metrics27 Sep 2026
- 2026The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization17 Jan 2026
- 2025Content Anonymization for Privacy in Long-form Audio14 Oct 2025
- 2024Exploratory Evaluation of Speech Content Masking8 Jan 2024
- 2023Adversarial Representation Learning for Robust Privacy Preservation in Audio29 Apr 2023