# The GDPR & Speech Data: Reflections of Legal and Technology Communities, First Steps towards a Common Understanding

arXiv: https://arxiv.org/abs/1907.03458 · HTML: https://mlsanity.com/paper/1907.03458/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 19 of 19)

- [The VoicePrivacy 2020 Challenge: Results and findings](https://mlsanity.com/paper/2109.00648/) (2021)
- [The VoicePrivacy 2022 Challenge Evaluation Plan](https://mlsanity.com/paper/2203.12468/) (2022)
- [Differentially Private Speaker Anonymization](https://mlsanity.com/paper/2202.11823/) (2022)
- [Improving speaker de-identification with functional data analysis of f0 trajectories](https://mlsanity.com/paper/2203.16738/) (2022)
- [Are disentangled representations all you need to build speaker anonymization systems?](https://mlsanity.com/paper/2208.10497/) (2022)
- [The VoicePrivacy 2020 Challenge Evaluation Plan](https://mlsanity.com/paper/2205.07123/) (2022)
- [Do Not Mimic My Voice: Speaker Identity Unlearning for Zero-Shot Text-to-Speech](https://mlsanity.com/paper/2507.20140/) (2025)
- ["Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding](https://mlsanity.com/paper/2505.15700/) (2025)
- [On the invertibility of a voice privacy system using embedding alignement](https://mlsanity.com/paper/2110.05431/) (2021)
- [Exploratory Evaluation of Speech Content Masking](https://mlsanity.com/paper/2401.03936/) (2024)
- [Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems](https://mlsanity.com/paper/2507.15214/) (2025)
- [Retrieving Speaker Information from Personalized Acoustic Models for Speech Recognition](https://mlsanity.com/paper/2111.04194/) (2021)
- [Protecting Bystander Privacy via Selective Hearing in Audio LLMs](https://mlsanity.com/paper/2512.06380/) (2025)
- [Evaluating X-vector-based Speaker Anonymization under White-box Assessment](https://mlsanity.com/paper/2109.11946/) (2021)
- [Self-Supervised Speech Representations Preserve Speech Characteristics while Anonymizing Voices](https://mlsanity.com/paper/2204.01677/) (2022)
- [Evaluation of Speaker Anonymization on Emotional Speech](https://mlsanity.com/paper/2305.01759/) (2023)
- [Target speaker anonymization in multi-speaker recordings](https://mlsanity.com/paper/2510.09307/) (2025)
- [Anonymization, Not Elimination: Utility-Preserved Speech Anonymization](https://mlsanity.com/paper/2604.17000/) (2026)
- [Is Word Error Rate Enough? Rethinking Privacy Evaluation in Speech with Entity-Aware Metrics](https://mlsanity.com/paper/2610.08831/) (2026)

## Abstract (arXiv)

Privacy preservation and the protection of speech data is in high demand, not least as a result of recent regulation, e.g. the General Data Protection Regulation (GDPR) in the EU. While there has been a period with which to prepare for its implementation, its implications for speech data is poorly understood. This assertion applies to both the legal and technology communities, and is hardly surprising since there is no universal definition of 'privacy', let alone a clear understanding of when or how the GDPR applies to the capture, storage and processing of speech data. In aiming to initiate the discussion that is needed to establish a level of harmonisation that is thus far lacking, this contribution presents some reflections of both legal and technology communities on the implications of the GDPR as regards speech data. The article outlines the need for taxonomies at the intersection of speech technology and data privacy - a discussion that is still very much in its infancy - and describes the ways to safeguards and priorities for future research. In being agnostic to any specific application, the treatment should be of interest to the speech communication community at large.