Relevant bibliographies by topics / Speaker anonymization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Speaker anonymization'

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

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Journal articles on the topic "Speaker anonymization"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Speaker anonymization"

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Champion, Pierre. "Anonymizing Speech : Evaluating and Designing Speaker Anonymization Techniques." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0101.

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L'essor de l'utilisation d'assistants vocaux, présents dans les téléphones, automobiles et autres, a augmenté la quantité de données de parole collectées et stockées. Bien que cette collecte de données soit cruciale pour entrainer les modèles qui traitent la parole, cette collecte soulève également des préoccupations de protection de la vie privée. Des technologies de pointe traitant la parole, telles que le clonage vocal et la reconnaissance d'attributs personnels (telles que l'identité, l'émotion, l'âge, le genre, etc.), peuvent être exploitées pour accéder et utiliser des informations perso
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Srivastava, Brij Mohan Lal. "Anonymisation du locuteur : représentation, évaluation et garanties formelles." Thesis, Université de Lille (2018-2021), 2021. https://pepite-depot.univ-lille.fr/LIBRE/EDMADIS/2021/2021LILUB029.pdf.

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L'émergence et la généralisation des interfaces vocales présentesdans les téléphones, les applications mobiles et les assistantsnumériques ont permis de faciliter la communication entre les citoyens,utilisateurs d'un service, et les prestataires de services. Citons àtitre d'exemple l'utilisation de mots de passe vocaux pour lesopérations bancaires, des haut-parleurs intelligents personnalisés, etc.Pour réaliser ces innovations, la collecte massive de données vocalesest essentielle aux entreprises comme aux chercheurs. Mais le stockagecentralisé à grande échelle des données vocales pose de grav
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Book chapters on the topic "Speaker anonymization"

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Champion, Pierre, Denis Jouvet, and Anthony Larcher. "Evaluating X-Vector-Based Speaker Anonymization Under White-Box Assessment." In Speech and Computer. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87802-3_10.

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Liu, Wei, Jiakang Li, Chunyu Wei, Meng Sun, Xiongwei Zhang, and Yongqiang Li. "A Novel Method to Evaluate the Privacy Protection in Speaker Anonymization." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06788-4_51.

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Conference papers on the topic "Speaker anonymization"

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Bauer, Luke A., Wenxuan Bao, Malvika Jadhav, and Vincent Bindschaedler. "Inference Attacks for X-Vector Speaker Anonymization." In 2025 IEEE Security and Privacy Workshops (SPW). IEEE, 2025. https://doi.org/10.1109/spw67851.2025.00020.

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Mawalim, Candy Olivia, Aulia Adila, and Masashi Unoki. "Fine-tuning TitaNet-Large Model for Speaker Anonymization Attacker Systems." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888822.

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Gaznepoglu, Ünal Ege, and Nils Peters. "Why disentanglement-based speaker anonymization systems fail at preserving emotions?" In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889709.

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Zhang, Yanzhe, Zhonghao Bi, Feiyang Xiao, Xuefeng Yang, Qiaoxi Zhu, and Jian Guan. "Attacking Voice Anonymization Systems with Augmented Feature and Speaker Identity Difference." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889535.

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Cai, Zexin, Henry Li Xinyuan, Ashi Garg, et al. "Privacy Versus Emotion Preservation Trade-Offs in Emotion-Preserving Speaker Anonymization." In 2024 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2024. https://doi.org/10.1109/slt61566.2024.10832351.

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Tomashenko, Natalia, Emmanuel Vincent, and Marc Tommasi. "Analysis of Speech Temporal Dynamics in the Context of Speaker Verification and Voice Anonymization." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887896.

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Zhao, Feiyu, Jianguo Wei, Wenhuan Lu, and Yongwei Li. "End-To-End Speaker Anonymization Based on Location-Variable Convolution and Multi-Head Self-Attention." In 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2024. https://doi.org/10.1109/trustcom63139.2024.00238.

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Meyer, Sarina, Florian Lux, Pavel Denisov, Julia Koch, Pascal Tilli, and Ngoc Thang Vu. "Speaker Anonymization with Phonetic Intermediate Representations." In Interspeech 2022. ISCA, 2022. http://dx.doi.org/10.21437/interspeech.2022-10703.

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Agarwal, Ayush, Amitabh Swain, and S. R. Mahadeva Prasanna. "Speaker Anonymization for Machines using Sinusoidal Model." In 2022 IEEE International Conference on Signal Processing and Communications (SPCOM). IEEE, 2022. http://dx.doi.org/10.1109/spcom55316.2022.9840792.

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Prajapati, Gauri P., Dipesh K. Singh, and Hemant A. Patil. "Significance of Distance Measures for Speaker Anonymization." In 2022 IEEE International Conference on Signal Processing and Communications (SPCOM). IEEE, 2022. http://dx.doi.org/10.1109/spcom55316.2022.9840515.

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