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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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Evaluation of Speaker Anonymization on Emotional Speech ; Analyse de l'anonymisation du locuteur sur de la parole émotionnelle
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In: JEP2022 - Journées d'Études sur la Parole ; https://hal.archives-ouvertes.fr/hal-03636737 ; JEP2022 - Journées d'Études sur la Parole, Jun 2022, Île de Noirmoutier, France (2022)
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Abstract:
International audience ; Speech data carries personal information, such as the speaker’s identity and emotional state, which can be used for malicious purposes. Among the studies that have addressed the topic of speech privacy, the VoicePrivacy initiative aims to promote the development of privacy tools for speechtechnologies. The goal of the VoicePrivacy Challenge 2020 (VPC) was to hide the source speaker’s identity while preserving the linguistic information. This paper studies the impact of the VPC speaker anonymization system on the emotional information of speech. Modifications to the system were also added in an attempt to mask the emotions while limiting signal degradation. Our results show that VPC baseline slightly masks the speakers’ emotions, but some modifications can mask the emotions more effectively while maintaining good intelligibility. ; Les données vocales contiennent des informations personnelles, telles que l'identité du locuteur ou son état émotionnel, pouvant être utilisées à des fins malveillantes. Parmi les études ayant abordé le sujet de la préservation de la confidentialité de la parole, l'initiative VoicePrivacy vise à promouvoir le développement d'outils de préservation de la vie privée pour les technologies vocales. L'objectif du VoicePrivacy Challenge 2020 (VPC) était de cacher l'identité du locuteur source tout en préservant les informations linguistiques. Cet article étudie l'impact du système d'anonymisation du locuteur du VPC sur les informations émotionnelles de la parole. Des modifications du système ont aussi été ajoutées pour essayer de masquer les émotions tout en limitant la dégradation du signal. Nos résultats montrent que la baseline du VPC masque légèrement les émotions des locuteurs mais certaines modifications permettent de masquer plus efficacement les émotions tout en conservant une bonne intelligibilité.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; Anonymisation du locuteur; Emotion Recognition; Privacité; Reconnaissance d'émotion; Speaker Anonymization; Voice Privacy
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URL: https://hal.archives-ouvertes.fr/hal-03636737/document
https://hal.archives-ouvertes.fr/hal-03636737
https://hal.archives-ouvertes.fr/hal-03636737/file/JEP2022_Emotion_Final.pdf
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| 3 |
Utterance partitioning for speaker recognition: an experimental review and analysis with new findings under GMM-SVM framework
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In: ISSN: 1381-2416 ; EISSN: 1572-8110 ; International Journal of Speech Technology ; https://hal.archives-ouvertes.fr/hal-03232723 ; International Journal of Speech Technology, Springer Verlag, In press, ⟨10.1007/s10772-021-09862-8⟩ (2021)
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Speaker Attentive Speech Emotion Recognition
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In: Proccedings of interspeech 2021 ; Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03554368 ; Interspeech 2021, Aug 2021, Brno, Czech Republic. pp.2866-2870, ⟨10.21437/interspeech.2021-573⟩ (2021)
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| 5 |
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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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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| 11 |
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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| 15 |
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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| 16 |
Kurdish spoken dialect recognition using x-vector speaker embeddings
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In: https://hal.archives-ouvertes.fr/hal-03262435 ; 2021 (2021)
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| 17 |
Evaluation of Speaker Anonymization on Emotional Speech
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In: 1st ISCA Symposium on Security and Privacy in Speech Communication ; https://hal.inria.fr/hal-03377797 ; 1st ISCA Symposium on Security and Privacy in Speech Communication, Nov 2021, Virtual, Germany (2021)
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| 18 |
An investigation into variability conditions in the SRE 2004 and 2008 Corpora ...
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