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Journal articles on the topic 'Speaker anonymization'

Author: Grafiati
Published: 21 May 2022
Last updated: 30 July 2025

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1

Shahin Shamsabadi, Ali, Brij Mohan Lal Srivastava, Aurélien Bellet, et al. "Differentially Private Speaker Anonymization." Proceedings on Privacy Enhancing Technologies 2023, no. 1 (2023): 98–114. http://dx.doi.org/10.56553/popets-2023-0007.

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Sharing real-world speech utterances is key to the training and deployment of voice-based services. However, it also raises privacy risks as speech contains a wealth of personal data. Speaker anonymization aims to remove speaker information from a speech utterance while leaving its linguistic and prosodic attributes intact. State-of-the-art techniques operate by disentangling the speaker information (represented via a speaker embedding) from these attributes and re-synthesizing speech based on the speaker embedding of another speaker. Prior research in the privacy community has shown that anon
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2

Matassoni, Marco, Seraphina Fong, and Alessio Brutti. "Speaker Anonymization: Disentangling Speaker Features from Pre-Trained Speech Embeddings for Voice Conversion." Applied Sciences 14, no. 9 (2024): 3876. http://dx.doi.org/10.3390/app14093876.

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Speech is a crucial source of personal information, and the risk of attackers using such information increases day by day. Speaker privacy protection is crucial, and various approaches have been proposed to hide the speaker’s identity. One approach is voice anonymization, which aims to safeguard speaker identity while maintaining speech content through techniques such as voice conversion or spectral feature alteration. The significance of voice anonymization has grown due to the necessity to protect personal information in applications such as voice assistants, authentication, and customer sup
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3

Turner, Henry, Giulio Lovisotto, and Ivan Martinovic. "Generating identities with mixture models for speaker anonymization." Computer Speech & Language 72 (March 2022): 101318. http://dx.doi.org/10.1016/j.csl.2021.101318.

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4

Yoo, In-Chul, Keonnyeong Lee, Seonggyun Leem, Hyunwoo Oh, Bonggu Ko, and Dongsuk Yook. "Speaker Anonymization for Personal Information Protection Using Voice Conversion Techniques." IEEE Access 8 (2020): 198637–45. http://dx.doi.org/10.1109/access.2020.3035416.

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5

Miao, Xiaoxiao, Yuxiang Zhang, Xin Wang, Natalia Tomashenko, Donny Cheng Lock Soh, and Ian Mcloughlin. "Adapting general disentanglement-based speaker anonymization for enhanced emotion preservation." Computer Speech & Language 94 (November 2025): 101810. https://doi.org/10.1016/j.csl.2025.101810.

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6

Mawalim, Candy Olivia, Kasorn Galajit, Jessada Karnjana, Shunsuke Kidani, and Masashi Unoki. "Speaker anonymization by modifying fundamental frequency and x-vector singular value." Computer Speech & Language 73 (May 2022): 101326. http://dx.doi.org/10.1016/j.csl.2021.101326.

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7

Yao, Jixun, Qing Wang, Pengcheng Guo, Ziqian Ning, and Lei Xie. "Distinctive and Natural Speaker Anonymization via Singular Value Transformation-Assisted Matrix." IEEE/ACM Transactions on Audio, Speech, and Language Processing 32 (2024): 2944–56. http://dx.doi.org/10.1109/taslp.2024.3407600.

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8

Maida, Carl A., Marvin Marcus, Di Xiong, et al. "Investigating Perceptions of Teachers and School Nurses on Child and Adolescent Oral Health in Los Angeles County." International Journal of Environmental Research and Public Health 19, no. 8 (2022): 4722. http://dx.doi.org/10.3390/ijerph19084722.

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This study reports the results of focus groups with school nurses and teachers from elementary, middle, and high schools to explore their perceptions of child and adolescent oral health. Participants included 14 school nurses and 15 teachers (83% female; 31% Hispanic; 21% White; 21% Asian; 14% African American; and 13% Others). Respondents were recruited from Los Angeles County schools and scheduled by school level for six one-hour focus groups using Zoom. Audio recordings were transcribed, reviewed, and saved with anonymization of speaker identities. NVivo software (QSR International, Melbour
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9

Kang, Wonjune, Margaret A. Hughes, and Deb Roy. "Anonymization of Voices in Spaces for Civic Dialogue: Measuring Impact on Empathy, Trust, and Feeling Heard." Proceedings of the ACM on Human-Computer Interaction 8, CSCW2 (2024): 1–22. http://dx.doi.org/10.1145/3687021.

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Anonymity is a powerful component of many participatory media platforms that can afford people greater freedom of expression and protection from external coercion and interference. However, it can be difficult to effectively implement on platforms that leverage spoken language due to distinct biomarkers present in the human voice. In this work, we explore the use of voice anonymization methods within the context of a technology-enhanced civic dialogue network based in the United States, whose purpose is to increase feelings of agency and being heard within civic processes. Specifically, we inv
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10

Tayebi Arasteh, Soroosh, Tomás Arias-Vergara, Paula Andrea Pérez-Toro, et al. "Addressing challenges in speaker anonymization to maintain utility while ensuring privacy of pathological speech." Communications Medicine 4, no. 1 (2024). http://dx.doi.org/10.1038/s43856-024-00609-5.

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Abstract Background Integration of speech into healthcare has intensified privacy concerns due to its potential as a non-invasive biomarker containing individual biometric information. In response, speaker anonymization aims to conceal personally identifiable information while retaining crucial linguistic content. However, the application of anonymization techniques to pathological speech, a critical area where privacy is especially vital, has not been extensively examined. Methods This study investigates anonymization’s impact on pathological speech across over 2700 speakers from multiple Ger
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11

AlJa’fari, Aya, Amjed Al-Mousa, and Iyad Jafar. "Speaker anonymization using generative adversarial networks." Journal of Intelligent & Fuzzy Systems, June 9, 2023, 1–15. http://dx.doi.org/10.3233/jifs-223642.

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The advent use of smart devices has enabled the emergence of many applications that facilitate user interaction through speech. However, speech reveals private and sensitive information about the user’s identity, posing several security risks. For example, a speaker’s speech can be acquired and used in speech synthesis systems to generate fake speech recordings that can be used to attack that speaker’s verification system. One solution is to anonymize the speaker’s identity from speech before using it. Existing anonymization schemes rely on using a pool of real speakers’ identities for anonymi
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12

Miao, Xiaoxiao, Ruijie Tao, Chang Zeng, and Xin Wang. "A Benchmark for Multi-speaker Anonymization." IEEE Transactions on Information Forensics and Security, 2025, 1. https://doi.org/10.1109/tifs.2025.3556345.

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13

Miao, Xiaoxiao, Xin Wang, Erica Cooper, Junichi Yamagishi, and Natalia Tomashenko. "Speaker Anonymization using Orthogonal Householder Neural Network." IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023, 1–15. http://dx.doi.org/10.1109/taslp.2023.3313429.

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14

Chen, Liping, Wenju Gu, Kong Aik Lee, Wu Guo, and Zhen-Hua Ling. "Pseudo-speaker Distribution Learning in Voice Anonymization." IEEE Transactions on Audio, Speech and Language Processing, 2025, 1–14. https://doi.org/10.1109/taslp.2024.3519879.

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15

Chang, Hyung-pil, In-Chul Yoo, Changhyeon Jeong, and Dongsuk Yook. "Zero-Shot Unseen Speaker Anonymization Via Voice Conversion." IEEE Access, 2022, 1. http://dx.doi.org/10.1109/access.2022.3227963.

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16

Yao, Jixun, Qing Wang, Pengcheng Guo, et al. "MUSA: Multi-Lingual Speaker Anonymization via Serial Disentanglement." IEEE Transactions on Audio, Speech and Language Processing, 2025, 1–11. https://doi.org/10.1109/taslpro.2025.3555115.

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17

Srivastava, Brij Mohan Lal, Mohamed Maouche, Md Sahidullah, et al. "Privacy and utility of x-vector based speaker anonymization." IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2022, 1–13. http://dx.doi.org/10.1109/taslp.2022.3190741.

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18

Wang, Rui, Liping Chen, Kong Aik Lee, and Zhen-Hua Ling. "Asynchronous Voice Anonymization by Learning from Speaker-Adversarial Speech." IEEE Signal Processing Letters, 2025, 1–5. https://doi.org/10.1109/lsp.2025.3563306.

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19

Chen, Liping, Chenyang Guo, Rui Wang, Kong Aik Lee, and Zhen-Hua Ling. "Any-to-any Speaker Attribute Perturbation for Asynchronous Voice Anonymization." IEEE Transactions on Information Forensics and Security, 2025, 1. https://doi.org/10.1109/tifs.2025.3592553.

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20

Wubet, Yeshanew Ale, and Kuang-Yow Lian. "Speaker Anonymization for Voice Biometrics Protection Using Voice Conversion and Multi-Target Speaker Voice Fusion." IEEE Transactions on Information Forensics and Security, 2025, 1. https://doi.org/10.1109/tifs.2025.3577023.

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21

Ahangaran, Meysam, Nauman Dawalatabad, Cody Karjadi, James Glass, Rhoda Au, and Vijaya B. Kolachalama. "Machine learning for privacy‐protected voice analysis in dementia assessment." Alzheimer's & Dementia 20, S2 (2024). https://doi.org/10.1002/alz.091272.

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AbstractBackgroundThe prevalence of cognitive impairments, such as mild cognitive impairment (MCI) and Alzheimer's disease (AD), has surged, necessitating rapid, cost‐effective, and non‐invasive diagnostic tools. Speech, as a rich source of cognitive indices, offers a promising avenue for distinguishing between healthy controls, MCI, and AD groups. However, the utilization of voice data poses privacy challenges, as speaker identities can be discerned through automatic speaker verification systems.MethodWe developed a machine learning framework for dementia assessment, utilizing acoustic featur
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22

Makoshi, Stephen Mikah. "Cyber Warfare and Nation-State Attack: Investigating Tactics, Techniques, and Procedures (TTPs) of State-Sponsored Cyberattacks and Defense Mechanisms By Stephen Mikah Makoshi May, 2025." May 14, 2025. https://doi.org/10.5281/zenodo.15412985.

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<strong><em>Cyber Warfare and Nation-State Attack: Investigating Tactics, Techniques, and Procedures (TTPs) of State-Sponsored Cyberattacks and Defense Mechanisms</em></strong> <strong>Author</strong>: Stephen Mikah Makoshi <strong>Publication Date</strong>: May 2025 <strong>Overview</strong> The manuscript <em>Cyber Warfare and Nation-State Attack: Investigating Tactics, Techniques, and Procedures (TTPs) of State-Sponsored Cyberattacks and Defense Mechanisms</em>, authored by Stephen Mikah Makoshi, is a rigorous and timely exploration of the escalating domain of cyber warfare, focusing on sta
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