# SegReConcat: A Data Augmentation Method for Voice Anonymization Attack

arXiv: https://arxiv.org/abs/2508.18907 · HTML: https://mlsanity.com/paper/2508.18907/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 42 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [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)
- [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)
- [Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy](https://mlsanity.com/paper/2210.07002/) (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)
- [Differentially Private Speaker Anonymization](https://mlsanity.com/paper/2202.11823/) (2022)
- [Language-Independent Speaker Anonymization Approach using Self-Supervised Pre-Trained Models](https://mlsanity.com/paper/2202.13097/) (2022)
- [NPU-NTU System for Voice Privacy 2024 Challenge](https://mlsanity.com/paper/2409.04173/) (2024)
- [MUSA: Multi-lingual Speaker Anonymization via Serial Disentanglement](https://mlsanity.com/paper/2407.11629/) (2024)
- [Privacy versus Emotion Preservation Trade-offs in Emotion-Preserving Speaker Anonymization](https://mlsanity.com/paper/2409.03655/) (2024)
- [HLTCOE JHU Submission to the Voice Privacy Challenge 2024](https://mlsanity.com/paper/2409.08913/) (2024)
- [A Benchmark for Multi-speaker Anonymization](https://mlsanity.com/paper/2407.05608/) (2024)
- [Distinctive and Natural Speaker Anonymization via Singular Value Transformation-assisted Matrix](https://mlsanity.com/paper/2405.10786/) (2024)
- [NTU-NPU System for Voice Privacy 2024 Challenge](https://mlsanity.com/paper/2410.02371/) (2024)
- [EASY: Emotion-aware Speaker Anonymization via Factorized Distillation](https://mlsanity.com/paper/2505.15004/) (2025)
- [Private kNN-VC: Interpretable Anonymization of Converted Speech](https://mlsanity.com/paper/2505.17584/) (2025)
- [Anonymising Elderly and Pathological Speech: Voice Conversion Using DDSP and Query-by-Example](https://mlsanity.com/paper/2410.15500/) (2024)
- [Analysis of Speech Temporal Dynamics in the Context of Speaker Verification and Voice Anonymization](https://mlsanity.com/paper/2412.17164/) (2024)
- [Attacking Voice Anonymization Systems with Augmented Feature and Speaker Identity Difference](https://mlsanity.com/paper/2412.19068/) (2024)
- [SpecWav-Attack: Leveraging Spectrogram Resizing and Wav2Vec 2.0 for Attacking Anonymized Speech](https://mlsanity.com/paper/2505.09616/) (2025)
- [Adapting General Disentanglement-Based Speaker Anonymization for Enhanced Emotion Preservation](https://mlsanity.com/paper/2408.05928/) (2024)
- [Why disentanglement-based speaker anonymization systems fail at preserving emotions?](https://mlsanity.com/paper/2501.13000/) (2025)
- [Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems](https://mlsanity.com/paper/2507.15214/) (2025)

## Built on by (most cited first, 1 of 1)

- [DAST: A Dual-Stream Voice Anonymization Attacker with Staged Training](https://mlsanity.com/paper/2603.12840/) (2026)

## Abstract (arXiv)

Anonymization of voice seeks to conceal the identity of the speaker while maintaining the utility of speech data. However, residual speaker cues often persist, which pose privacy risks. We propose SegReConcat, a data augmentation method for attacker-side enhancement of automatic speaker verification systems. SegReConcat segments anonymized speech at the word level, rearranges segments using random or similarity-based strategies to disrupt long-term contextual cues, and concatenates them with the original utterance, allowing an attacker to learn source speaker traits from multiple perspectives. The proposed method has been evaluated in the VoicePrivacy Attacker Challenge 2024 framework across seven anonymization systems, SegReConcat improves de-anonymization on five out of seven systems.