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1
Differentially private speaker anonymization
In: https://hal.inria.fr/hal-03588932 ; 2022 (2022)
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2
Privacy and utility of x-vector based speaker anonymization
In: https://hal.inria.fr/hal-03197376 ; 2021 (2021)
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3
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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4
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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5
The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
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6
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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7
The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
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8
Enhancing Speech Privacy with Slicing
In: https://hal.inria.fr/hal-03369137 ; 2021 (2021)
Abstract: Privacy preservation calls for speech anonymization methods which hide the speaker's identity while minimizing the impact on downstream tasks such as automatic speech recognition (ASR) training or decoding. In the recent VoicePrivacy 2020 Challenge, several anonymization methods have been proposed to transform speech utterances in a way that preserves their verbal and prosodic contents while reducing the accuracy of a speaker verification system. In this paper, we propose to further increase the privacy achieved by such methods by segmenting the utterances into shorter slices. We show that our approach has two major impacts on privacy. First, it reduces the accuracy of speaker verification with respect to unsegmented utterances. Second, it also reduces the amount of personal information that can be extracted from the verbal content, in a way that cannot easily be reversed by an attacker. We also show that it is possible to train an ASR system from anonymized speech slices with negligible impact on the word error rate.
Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; anonymization; automatic speech recognition; privacy; speaker verification
URL: https://hal.inria.fr/hal-03369137/document
https://hal.inria.fr/hal-03369137/file/main.pdf
https://hal.inria.fr/hal-03369137
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9
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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10
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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11
The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
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12
Privacy and utility of x-vector based speaker anonymization
In: https://hal.inria.fr/hal-03197376 ; 2021 (2021)
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13
Design Choices for X-vector Based Speaker Anonymization
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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14
A comparative study of speech anonymization metrics
In: INTERSPEECH 2020 ; https://hal.inria.fr/hal-02907918 ; INTERSPEECH 2020, Oct 2020, Shanghai, China (2020)
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