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RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION
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In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022)
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Differentially private speaker anonymization
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In: https://hal.inria.fr/hal-03588932 ; 2022 (2022)
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Privacy and utility of x-vector based speaker anonymization
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In: https://hal.inria.fr/hal-03197376 ; 2021 (2021)
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Enabling voice-based apps with European values
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In: ISSN: 0926-4981 ; ERCIM News ; https://hal.inria.fr/hal-03476390 ; ERCIM News, ERCIM, 2021, 126, pp.38-39 ; https://ercim-news.ercim.eu/images/stories/EN126/EN126-web.pdf (2021)
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Enhancing Speech Privacy with Slicing
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In: https://hal.inria.fr/hal-03369137 ; 2021 (2021)
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| 6 |
Privacy and utility of x-vector based speaker anonymization
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In: https://hal.inria.fr/hal-03197376 ; 2021 (2021)
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Evaluating Voice Conversion-based Privacy Protection against Informed Attackers
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In: ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing ; https://hal.inria.fr/hal-02355115 ; ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing, IEEE Signal Processing Society, May 2020, Barcelona, Spain. pp.2802-2806 (2020)
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Abstract:
International audience ; Speech data conveys sensitive speaker attributes like identity or accent. With a small amount of found data, such attributes can be inferred and exploited for malicious purposes: voice cloning, spoofing, etc. Anonymization aims to make the data unlinkable, i.e., ensure that no utterance can be linked to its original speaker. In this paper, we investigate anonymization methods based on voice conversion. In contrast to prior work, we argue that various linkage attacks can be designed depending on the attackers' knowledge about the anonymization scheme. We compare two frequency warping-based conversion methods and a deep learning based method in three attack scenarios. The utility of converted speech is measured via the word error rate achieved by automatic speech recognition, while privacy protection is assessed by the increase in equal error rate achieved by state-of-the-art i-vector or x-vector based speaker verification. Our results show that voice conversion schemes are unable to effectively protect against an attacker that has extensive knowledge of the type of conversion and how it has been applied, but may provide some protection against less knowledgeable attackers.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; attacker; linkage attack; privacy; speaker verification; speech recognition; voice conversion
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URL: https://hal.inria.fr/hal-02355115v2/document
https://hal.inria.fr/hal-02355115
https://hal.inria.fr/hal-02355115v2/file/ppvc_final.pdf
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| 8 |
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
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In: AISTATS 2020 - The 23rd International Conference on Artificial Intelligence and Statistics ; https://hal.inria.fr/hal-03100057 ; AISTATS 2020 - The 23rd International Conference on Artificial Intelligence and Statistics, Aug 2020, Palerme / Virtual, Italy ; https://aistats.org/aistats2020/ (2020)
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Design Choices for X-vector Based Speaker Anonymization
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In: INTERSPEECH 2020 ; https://hal.archives-ouvertes.fr/hal-02610447 ; INTERSPEECH 2020, International Speech Communication Association (ISCA), Oct 2020, Shanghai, China (2020)
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A comparative study of speech anonymization metrics
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In: INTERSPEECH 2020 ; https://hal.inria.fr/hal-02907918 ; INTERSPEECH 2020, Oct 2020, Shanghai, China (2020)
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Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?
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In: INTERSPEECH 2019 - 20th Annual Conference of the International Speech Communication Association ; https://hal.inria.fr/hal-02166434 ; INTERSPEECH 2019 - 20th Annual Conference of the International Speech Communication Association, Sep 2019, Graz, Austria (2019)
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