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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 – 14 of 14 1 Differentially private speaker anonymization Shamsabadi, Ali Shahin; Srivastava, Brij Mohan Lal; Bellet, Aurélien... In: https://hal.inria.fr/hal-03588932 ; 2022 (2022) BASE Show details 2 Privacy and utility of x-vector based speaker anonymization Srivastava, Brij Mohan Lal; Maouche, Mohamed; Sahidullah, Md... In: https://hal.inria.fr/hal-03197376 ; 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 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 5 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 6 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 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 Enhancing Speech Privacy with Slicing Maouche, Mohamed; Srivastava, Brij Mohan Lal; Vauquier, Nathalie... In: https://hal.inria.fr/hal-03369137 ; 2021 (2021) BASE Show details 9 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 10 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 11 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 12 Privacy and utility of x-vector based speaker anonymization Srivastava, Brij Mohan Lal; Maouche, Mohamed; Sahidullah, Md; Vincent, Emmanuel; Bellet, Aurélien; Tommasi, Marc; Tomashenko, Natalia; Wang, Xin; Yamagishi, Junichi In: https://hal.inria.fr/hal-03197376 ; 2021 (2021) Abstract: We study the scenario where individuals (speakers) contribute to the publication of an anonymized speech corpus. Data users then leverage this public corpus to perform downstream tasks (such as training automatic speech recognition systems), while attackers may try to de-anonymize itbased on auxiliary knowledge they collect. Motivated by this scenario, speaker anonymization aims to conceal the speaker identity while preserving the quality and usefulness of speech data. In this paper, we study x-vector based speaker anonymization, the leading approach in the recent Voice Privacy Challenge, which converts an input utterance into that of a random pseudo-speaker. We show that the strength of the anonymization varies significantly depending on how the pseudo-speaker is selected. In particular, we investigate four design choices: the distance measure between speakers, the region of x-vector space where the pseudo-speaker is mapped, the gender selection and whether to use speaker or utterance level assignment. We assess the quality of anonymization from the perspective of the three actors involved in our threat model, namely the speaker, the user and the attacker. To measure privacy and utility, we use respectively the linkability score achieved by the attackers and the decoding word error rate incurred by an ASR model trained with the anonymized data. Experiments on LibriSpeech dataset confirm that the optimal combination ofdesign choices yield state-of-the-art performance in terms of privacy protection as well as utility. Experiments on Mozilla Common Voice dataset show that the best design choices with 50 speakers guarantee the same anonymization level against re-identification attack as raw speech with 20,000 speakers. Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; linkability; privacy; speaker anonymization; speaker identification; speech recognition; utility URL: https://hal.inria.fr/hal-03197376v2/file/design_choices_informed.pdf https://hal.inria.fr/hal-03197376 https://hal.inria.fr/hal-03197376v2/document BASE Hide details 13 Design Choices for X-vector Based Speaker Anonymization Srivastava, Brij Mohan Lal; Tomashenko, Natalia; Wang, Xin... In: INTERSPEECH 2020 ; https://hal.archives-ouvertes.fr/hal-02610447 ; INTERSPEECH 2020, International Speech Communication Association (ISCA), Oct 2020, Shanghai, China (2020) BASE Show details 14 A comparative study of speech anonymization metrics Maouche, Mohamed; Srivastava, Brij Mohan Lal; Vauquier, Nathalie... In: INTERSPEECH 2020 ; https://hal.inria.fr/hal-02907918 ; INTERSPEECH 2020, Oct 2020, Shanghai, China (2020) BASE Show details

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