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    • ANR-18-CE23-0018,DEEP-PRIVACY,Apprentissage distribué, personnalisé, préservant la privacité pour le traitement de la parole(2018) (8)
    • ANR-18-JSTS-0001,VoicePersonae,Clonage et protection de l'identité vocale(2018) (8)
    • Avignon Université (AU)-Centre d'Enseignement et de Recherche en Informatique - CERI (8)
    • Laboratoire Informatique d'Avignon (LIA) (8)
    • Tomashenko, Natalia (8)
    • ANR-19-DATA-0008,Harpocrates,Open data, outils et challenges pour l'anonymisation des voix(2019) (7)
    • Bonastre, Jean-François (7)
    • Machine Learning in Information Networks (MAGNET) (7)
    • Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS) (6)
    • Chanclu, Anaïs (6)
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    • 2022 (1)
    • 2021 (7)
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    • Online (8)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION Mdhaffar, Salima; Bonastre, Jean-François; Tommasi, Marc; Tomashenko, Natalia; Estève, Yannick In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022) Abstract: International audience ; The widespread of powerful personal devices capable of collecting voice of their users has opened the opportunity to build speaker adapted speech recognition system (ASR) or to participate to collaborative learning of ASR. In both cases, personalized acoustic models (AM), i.e. fine-tuned AM with specific speaker data, can be built. A question that naturally arises is whether the dissemination of personalized acoustic models can leak personal information. In this paper, we show that it is possible to retrieve the gender of the speaker, but also his identity, by just exploiting the weight matrix changes of a neural acoustic model locally adapted to this speaker. Incidentally we observe phenomena that may be useful towards explainability of deep neural networks in the context of speech processing. Gender can be identified almost surely using only the first layers and speaker verification performs well when using middle-up layers. Our experimental study on the TED-LIUM 3 dataset with HMM/TDNN models shows an accuracy of 95% for gender detection, and an Equal Error Rate of 9.07% for a speaker verification task by only exploiting the weights from personalized models that could be exchanged instead of user data. Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; acoustic model; Automatic speech recognition; collaborative learning; personalized acoustic models; speaker information URL: https://hal.archives-ouvertes.fr/hal-03539741 https://hal.archives-ouvertes.fr/hal-03539741/document https://hal.archives-ouvertes.fr/hal-03539741/file/ICASSP_2022_SpeakerAnalysisInfoPrivacyVF.pdf BASE Hide details 2 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 3 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 4 The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021) BASE Show details 5 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 6 Benchmarking and challenges in security and privacy for voice biometrics Bonastre, Jean-Francois; Delgado, Hector; Evans, Nicholas... In: SPSC 2021, 1st ISCA Symposium on Security and Privacy in Speech Communication ; https://hal.archives-ouvertes.fr/hal-03346196 ; SPSC 2021, 1st ISCA Symposium on Security and Privacy in Speech Communication, ISCA, Nov 2021, Magdeburg, Germany. ⟨10.21437/SPSC.2021-11⟩ ; https://spsc-symposium2021.de/#home (2021) BASE Show details 7 The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021) BASE Show details 8 Anonymous speaker clusters: Making distinctions between anonymised speech recordings with clustering interface O'Brien, Benjamin; Tomashenko, Natalia; Chanclu, Anaïs... In: INTERSPEECH 2021 ; https://hal.archives-ouvertes.fr/hal-03267084 ; INTERSPEECH 2021, Aug 2021, Brno, Czech Republic (2021) BASE Show details

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