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Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
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Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (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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Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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The VoicePrivacy 2020 Challenge: Results and findings
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In: https://hal.archives-ouvertes.fr/hal-03332224 ; 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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Introducing the VoicePrivacy initiative
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In: INTERSPEECH 2020 ; https://hal.inria.fr/hal-02562199 ; INTERSPEECH 2020, Oct 2020, Shanghai, China (2020)
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Addressing Text-Dependent Speaker Verification Using Singing Speech
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In: Applied Sciences ; Volume 9 ; Issue 13 (2019)
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Phoneme specific modelling and scoring techniques for anti spoofing system
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Representation, classification and information fusion for robust and efficient multimodal human states recognition ...
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Li, Ming. - : University of Southern California Digital Library (USC.DL), 2015
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Residual Phase Cepstrum Coefficients with Application to Cross-lingual Speaker Verification
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In: Electrical and Computer Engineering Faculty Research and Publications (2012)
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How is individuality expressed in voice? An introduction to speech production & description for speaker classification
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In: In: Müller, C, (ed.) Speaker Classification I. (1 - 20). Springer Verlag: Berlin. (2007) (2007)
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Automatic speaker verification on site and by telephone: methods, applications and assessment
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Melin, Håkan. - : KTH, Tal, musik och hörsel, TMH, 2006. : Stockholm : KTH, 2006
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