# Target speaker anonymization in multi-speaker recordings

arXiv: https://arxiv.org/abs/2510.09307 · HTML: https://mlsanity.com/paper/2510.09307/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 33 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 25 of 25)

- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [LibriMix: An Open-Source Dataset for Generalizable Speech Separation](https://mlsanity.com/paper/2005.11262/) (2020)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [Music Source Separation with Band-split RNN](https://mlsanity.com/paper/2209.15174/) (2022)
- [Introducing the VoicePrivacy Initiative](https://mlsanity.com/paper/2005.01387/) (2020)
- [Speaker Anonymization Using X-vector and Neural Waveform Models](https://mlsanity.com/paper/1905.13561/) (2019)
- [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)
- [MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems](https://mlsanity.com/paper/2307.11394/) (2023)
- [Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques](https://mlsanity.com/paper/2308.04455/) (2023)
- [ECAPA2: A Hybrid Neural Network Architecture and Training Strategy for Robust Speaker Embeddings](https://mlsanity.com/paper/2401.08342/) (2024)
- [The GDPR & Speech Data: Reflections of Legal and Technology Communities, First Steps towards a Common Understanding](https://mlsanity.com/paper/1907.03458/) (2019)
- [Speaker anonymization using orthogonal Householder neural network](https://mlsanity.com/paper/2305.18823/) (2023)
- [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)
- [Improving speaker de-identification with functional data analysis of f0 trajectories](https://mlsanity.com/paper/2203.16738/) (2022)
- [MUSA: Multi-lingual Speaker Anonymization via Serial Disentanglement](https://mlsanity.com/paper/2407.11629/) (2024)
- [WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker Extraction](https://mlsanity.com/paper/2409.15799/) (2024)
- [A Benchmark for Multi-speaker Anonymization](https://mlsanity.com/paper/2407.05608/) (2024)
- [Libri2Vox Dataset: Target Speaker Extraction with Diverse Speaker Conditions and Synthetic Data](https://mlsanity.com/paper/2412.12512/) (2024)
- [Target Speaker Extraction with Curriculum Learning](https://mlsanity.com/paper/2406.07845/) (2024)
- [Voice Conversion-based Privacy through Adversarial Information Hiding](https://mlsanity.com/paper/2409.14919/) (2024)

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

- [The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization](https://mlsanity.com/paper/2601.11846/) (2026)

## Abstract (arXiv)

Most of the existing speaker anonymization research has focused on single-speaker audio, leading to the development of techniques and evaluation metrics optimized for such condition. This study addresses the significant challenge of speaker anonymization within multi-speaker conversational audio, specifically when only a single target speaker needs to be anonymized. This scenario is highly relevant in contexts like call centers, where customer privacy necessitates anonymizing only the customer's voice in interactions with operators. Conventional anonymization methods are often not suitable for this task. Moreover, current evaluation methodology does not allow us to accurately assess privacy protection and utility in this complex multi-speaker scenario. This work aims to bridge these gaps by exploring effective strategies for targeted speaker anonymization in conversational audio, highlighting potential problems in their development and proposing corresponding improved evaluation methodologies.