Target speaker anonymization in multi-speaker recordings
- URL: http://arxiv.org/abs/2510.09307v1
- Date: Fri, 10 Oct 2025 11:59:45 GMT
- Title: Target speaker anonymization in multi-speaker recordings
- Authors: Natalia Tomashenko, Junichi Yamagishi, Xin Wang, Yun Liu, Emmanuel Vincent,
- Abstract summary: This study addresses the significant challenge of speaker anonymization within multi-speaker conversational audio.<n>This scenario is highly relevant in contexts like call centers, where customer privacy necessitates anonymizing only the customer's voice.<n>This work aims to bridge these gaps by exploring effective strategies for targeted speaker anonymization in conversational audio.
- Score: 35.23403922131853
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Content Anonymization for Privacy in Long-form Audio [9.679458545535388]
Long-form audio is commonplace in domains such as interviews, phone calls, and meetings.<n> given multiple utterances from the same speaker, an attacker could exploit an individual's vocabulary, syntax, and turns of phrase.<n>We propose new content anonymization approaches to address this risk.
arXiv Detail & Related papers (2025-10-14T17:52:50Z) - Improving the Speaker Anonymization Evaluation's Robustness to Target Speakers with Adversarial Learning [12.642704894600602]
We propose to add a target classifier that measures the influence of target speaker information in the evaluation.<n>Experiments demonstrate that this approach is effective for multiple anonymizers.
arXiv Detail & Related papers (2025-08-13T13:38:09Z) - A Benchmark for Multi-speaker Anonymization [9.990701310620368]
We present an attempt to provide a multi-speaker anonymization benchmark.<n>We also discuss the privacy leakage of overlapping conversations.<n>Experiments conducted on both non-overlap simulated and real-world datasets demonstrate the effectiveness of the multi-speaker anonymization system.
arXiv Detail & Related papers (2024-07-08T04:48:43Z) - Voice Anonymization for All -- Bias Evaluation of the Voice Privacy
Challenge Baseline System [0.48342038441006807]
This study investigates bias in voice anonymization systems within the context of the Voice Privacy Challenge.
We curate a novel benchmark dataset to assess performance disparities among speaker subgroups based on sex and dialect.
arXiv Detail & Related papers (2023-11-27T13:26:49Z) - Anonymizing Speech: Evaluating and Designing Speaker Anonymization
Techniques [1.2691047660244337]
The growing use of voice user interfaces has led to a surge in the collection and storage of speech data.
This thesis proposes solutions for anonymizing speech and evaluating the degree of the anonymization.
arXiv Detail & Related papers (2023-08-05T16:14:17Z) - Question-Interlocutor Scope Realized Graph Modeling over Key Utterances
for Dialogue Reading Comprehension [61.55950233402972]
We propose a new key utterances extracting method for dialogue reading comprehension.
It performs prediction on the unit formed by several contiguous utterances, which can realize more answer-contained utterances.
As a graph constructed on the text of utterances, we then propose Question-Interlocutor Scope Realized Graph (QuISG) modeling.
arXiv Detail & Related papers (2022-10-26T04:00:42Z) - Speaker De-identification System using Autoencoders and Adversarial
Training [58.720142291102135]
We propose a speaker de-identification system based on adversarial training and autoencoders.
Experimental results show that combining adversarial learning and autoencoders increase the equal error rate of a speaker verification system.
arXiv Detail & Related papers (2020-11-09T19:22:05Z) - Design Choices for X-vector Based Speaker Anonymization [48.46018902334472]
We present a flexible pseudo-speaker selection technique as a baseline for the first VoicePrivacy Challenge.
Experiments are performed using datasets derived from LibriSpeech to find the optimal combination of design choices in terms of privacy and utility.
arXiv Detail & Related papers (2020-05-18T11:32:14Z) - Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention [70.82604384963679]
This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features.
We extract a speaker representation used for adaptation directly from the test utterance.
arXiv Detail & Related papers (2020-02-14T05:05:36Z) - Improving speaker discrimination of target speech extraction with
time-domain SpeakerBeam [100.95498268200777]
SpeakerBeam exploits an adaptation utterance of the target speaker to extract his/her voice characteristics.
SpeakerBeam sometimes fails when speakers have similar voice characteristics, such as in same-gender mixtures.
We show experimentally that these strategies greatly improve speech extraction performance, especially for same-gender mixtures.
arXiv Detail & Related papers (2020-01-23T05:36:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.