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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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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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| 9 |
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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Abstract:
International audience ; Speech anonymization techniques have recently been proposed for preserving speakers' privacy. They aim at concealing speak-ers' identities while preserving the spoken content. In this study, we compare three metrics proposed in the literature to assess the level of privacy achieved. We exhibit through simulation the differences and blindspots of some metrics. In addition, we conduct experiments on real data and state-of-the-art anonymiza-tion techniques to study how they behave in a practical scenario. We show that the application-independent log-likelihood-ratio cost function C min llr provides a more robust evaluation of privacy than the equal error rate (EER), and that detection-based metrics provide different information from linkability metrics. Interestingly , the results on real data indicate that current anonymiza-tion design choices do not induce a regime where the differences between those metrics become apparent.
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Keyword:
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; anonymization; privacy metrics; speaker recog- nition; voice conversion
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URL: https://hal.inria.fr/hal-02907918
https://hal.inria.fr/hal-02907918/document
https://hal.inria.fr/hal-02907918/file/anonymization_metrics_IS2020.pdf
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| 11 |
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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