# VoxTubeS: Distributable Speaker-Anonymized Synthetic Speech Corpora and Their Analysis

arXiv: https://arxiv.org/abs/2609.12432 · HTML: https://mlsanity.com/paper/2609.12432/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Anti-spoofing and adversarial audio attacks, one of 11 directions in Speech and audio language models.
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Placed in

- Speech and audio language models / [Anti-spoofing and adversarial audio attacks](https://mlsanity.com/speech/anti-spoofing-and-adversarial-audio-attacks/)

## Builds on (most cited first, 22 of 22)

- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [BigVGAN: A Universal Neural Vocoder with Large-Scale Training](https://mlsanity.com/paper/2206.04658/) (2022)
- [Open-Source Conversational AI with SpeechBrain 1.0](https://mlsanity.com/paper/2407.00463/) (2024)
- [The VoicePrivacy 2020 Challenge: Results and findings](https://mlsanity.com/paper/2109.00648/) (2021)
- [The VoicePrivacy 2024 Challenge Evaluation Plan](https://mlsanity.com/paper/2404.02677/) (2024)
- [A Unified Framework for Quantifying Privacy Risk in Synthetic Data](https://mlsanity.com/paper/2211.10459/) (2022)
- [Speaker anonymization using orthogonal Householder neural network](https://mlsanity.com/paper/2305.18823/) (2023)
- [The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation](https://mlsanity.com/paper/2407.11516/) (2024)
- [Deep Neural Networks for Automatic Speech Processing: A Survey from Large Corpora to Limited Data](https://mlsanity.com/paper/2003.04241/) (2020)
- [The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization](https://mlsanity.com/paper/2601.11846/) (2026)
- [SynVox2: Towards a privacy-friendly VoxCeleb2 dataset](https://mlsanity.com/paper/2309.06141/) (2023)
- [Study on the Fairness of Speaker Verification Systems on Underrepresented Accents in English](https://mlsanity.com/paper/2204.12649/) (2022)
- [Hiding speaker's sex in speech using zero-evidence speaker representation in an analysis/synthesis pipeline](https://mlsanity.com/paper/2211.16065/) (2022)
- [Voice Anonymization for All -- Bias Evaluation of the Voice Privacy Challenge Baseline System](https://mlsanity.com/paper/2311.15804/) (2023)
- [DAIEN-TTS: Disentangled Audio Infilling for Environment-Aware Text-to-Speech Synthesis](https://mlsanity.com/paper/2509.14684/) (2025)
- [SALT: Distinguishable Speaker Anonymization Through Latent Space Transformation](https://mlsanity.com/paper/2310.05051/) (2023)

## Abstract (arXiv)

Large speech corpora support research, but recordings can expose speaker identity because voice remains a recognizable biometric. Meanwhile, speech data derived from media can be difficult to redistribute reliably. We present VoxTubeS, a family of speaker-anonymized synthetic speech corpora designed for redistribution, comprising three method families and seven variants derived from the VoxTube corpus, which is distributed under CC BY-NC-SA 4.0, using 1.29M quality-filtered English utterances from 1,511 speakers. The synthesis methods span voice conversion, latent-space anonymization, and controllable text-to-speech. We evaluate VoxTubeS using utterance-level unlinkability, conversation-level linkability and singling-out, downstream speaker verification, linguistic consistency, speaker diversity, and fairness metrics for gender and accents. Our comprehensive analysis exposes a complex trade-off: stronger identity suppression often reduces linkability but sacrifices utility and population diversity, whereas speaker consistency training improves both utterance- and conversation-level privacy while retaining comparable utility and a broader speaker space. Fairness varies independently of aggregate performance. No method dominates; VoxTubeS therefore treats corpus construction as a choice among operating points that balances privacy, utility, diversity, fairness, and responsible redistribution under the source license.