# HLTCOE JHU Submission to the Voice Privacy Challenge 2024

arXiv: https://arxiv.org/abs/2409.08913 · HTML: https://mlsanity.com/paper/2409.08913/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [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)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Voice Conversion With Just Nearest Neighbors](https://mlsanity.com/paper/2305.18975/) (2023)
- [The VoicePrivacy 2024 Challenge Evaluation Plan](https://mlsanity.com/paper/2404.02677/) (2024)
- [Investigating on Incorporating Pretrained and Learnable Speaker Representations for Multi-Speaker Multi-Style Text-to-Speech](https://mlsanity.com/paper/2103.04088/) (2021)
- [VoicePrivacy 2022 System Description: Speaker Anonymization with Feature-matched F0 Trajectories](https://mlsanity.com/paper/2210.17338/) (2022)

## Built on by (most cited first, 10 of 10)

- [The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization](https://mlsanity.com/paper/2601.11846/) (2026)
- [Analysis of Speech Temporal Dynamics in the Context of Speaker Verification and Voice Anonymization](https://mlsanity.com/paper/2412.17164/) (2024)
- [TVTSyn: Content-Synchronous Time-Varying Timbre for Streaming Voice Conversion and Anonymization](https://mlsanity.com/paper/2602.09389/) (2026)
- [The First VoicePrivacy Attacker Challenge](https://mlsanity.com/paper/2504.14183/) (2025)
- [Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems](https://mlsanity.com/paper/2507.15214/) (2025)
- [The First VoicePrivacy Attacker Challenge Evaluation Plan](https://mlsanity.com/paper/2410.07428/) (2024)
- [SEF-MK: Speaker-Embedding-Free Voice Anonymization through Multi-k-means Quantization](https://mlsanity.com/paper/2508.07086/) (2025)
- [SegReConcat: A Data Augmentation Method for Voice Anonymization Attack](https://mlsanity.com/paper/2508.18907/) (2025)
- [Evaluating Identity Leakage in Speaker De-Identification Systems](https://mlsanity.com/paper/2508.14012/) (2025)
- [DiffAnon: Diffusion-based Prosody Control for Voice Anonymization](https://mlsanity.com/paper/2604.26281/) (2026)

## Abstract (arXiv)

We present a number of systems for the Voice Privacy Challenge, including voice conversion based systems such as the kNN-VC method and the WavLM voice Conversion method, and text-to-speech (TTS) based systems including Whisper-VITS. We found that while voice conversion systems better preserve emotional content, they struggle to conceal speaker identity in semi-white-box attack scenarios; conversely, TTS methods perform better at anonymization and worse at emotion preservation. Finally, we propose a random admixture system which seeks to balance out the strengths and weaknesses of the two category of systems, achieving a strong EER of over 40% while maintaining UAR at a respectable 47%.