# Children's Voice Privacy: First Steps And Emerging Challenges

arXiv: https://arxiv.org/abs/2506.00100 · HTML: https://mlsanity.com/paper/2506.00100/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 16 of 16)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Voice Conversion With Just Nearest Neighbors](https://mlsanity.com/paper/2305.18975/) (2023)
- [speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment](https://mlsanity.com/paper/2104.01378/) (2021)
- [HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement](https://mlsanity.com/paper/2203.13086/) (2022)
- [The VoicePrivacy 2024 Challenge Evaluation Plan](https://mlsanity.com/paper/2404.02677/) (2024)
- [Speaker anonymisation using the McAdams coefficient](https://mlsanity.com/paper/2011.01130/) (2020)
- [Design Choices for X-vector Based Speaker Anonymization](https://mlsanity.com/paper/2005.08601/) (2020)
- [Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques](https://mlsanity.com/paper/2308.04455/) (2023)
- [Speaker anonymization using neural audio codec language models](https://mlsanity.com/paper/2309.14129/) (2023)
- [The Impact of Speech Anonymization on Pathology and Its Limits](https://mlsanity.com/paper/2404.08064/) (2024)
- [Analysis of Disfluency in Children's Speech](https://mlsanity.com/paper/2010.04293/) (2020)
- [The VoicePrivacy 2020 Challenge Evaluation Plan](https://mlsanity.com/paper/2205.07123/) (2022)
- [Rhythm Modeling for Voice Conversion](https://mlsanity.com/paper/2307.06040/) (2023)
- [Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR](https://mlsanity.com/paper/2501.10256/) (2025)

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

- [Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models](https://mlsanity.com/paper/2509.02859/) (2025)
- [The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization](https://mlsanity.com/paper/2601.11846/) (2026)
- [Do Compact SSL Backbones Matter for Audio Deepfake Detection? A Controlled Study with RAPTOR](https://mlsanity.com/paper/2603.06164/) (2026)
- [Interpreting Speaker Characteristics in the Dimensions of Self-Supervised Speech Features](https://mlsanity.com/paper/2603.03096/) (2026)
- [Child-Centric Voice Anonymization in Single and Multi-Speaker Speech via Domain-Adapted SSL Models](https://mlsanity.com/paper/2606.29897/) (2026)

## Abstract (arXiv)

Children are one of the most under-represented groups in speech technologies, as well as one of the most vulnerable in terms of privacy. Despite this, anonymization techniques targeting this population have received little attention. In this study, we seek to bridge this gap, and establish a baseline for the use of voice anonymization techniques designed for adult speech when applied to children's voices. Such an evaluation is essential, as children's speech presents a distinct set of challenges when compared to that of adults. This study comprises three children's datasets, six anonymization methods, and objective and subjective utility metrics for evaluation. Our results show that existing systems for adults are still able to protect children's voice privacy, but suffer from much higher utility degradation. In addition, our subjective study displays the challenges of automatic evaluation methods for speech quality in children's speech, highlighting the need for further research.