| 1 |
RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION
|
|
|
|
In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Evaluation of Speaker Anonymization on Emotional Speech ; Analyse de l'anonymisation du locuteur sur de la parole émotionnelle
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Utterance partitioning for speaker recognition: an experimental review and analysis with new findings under GMM-SVM framework
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Speaker Attentive Speech Emotion Recognition
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Privacy and utility of x-vector based speaker anonymization
|
|
|
|
In: https://hal.inria.fr/hal-03197376 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
The VoicePrivacy 2020 Challenge: Results and findings
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
The VoicePrivacy 2020 Challenge: Results and findings
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Enhancing Speech Privacy with Slicing
|
|
|
|
In: https://hal.inria.fr/hal-03369137 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 14 |
The VoicePrivacy 2020 Challenge: Results and findings
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
Privacy and utility of x-vector based speaker anonymization
|
|
|
|
In: https://hal.inria.fr/hal-03197376 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Kurdish spoken dialect recognition using x-vector speaker embeddings
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03262435 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Evaluation of Speaker Anonymization on Emotional Speech
|
|
|
|
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)
|
|
Abstract:
International audience ; Speech data carries a range of personal information, such as the speaker's identity and emotional state. These attributes can be used for malicious purposes. With the development of virtual assistants, a new generation of privacy threats has emerged. Current studies have addressed the topic of preserving speech privacy. One of them, the VoicePrivacy initiative aims to promote the development of privacy preservation tools for speech technology. The task selected for the VoicePrivacy 2020 Challenge (VPC) is about speaker anonymization. The goal is to hide the source speaker's identity while preserving the linguistic information. The baseline of the VPC makes use of a voice conversion. This paper studies the impact of the speaker anonymization baseline system of the VPC on emotional information present in speech utterances. Evaluation is performed following the VPC rules regarding the attackers' knowledge about the anonymization system. Our results show that the VPC baseline system does not suppress speakers' emotions against informed attackers. When comparing anonymized speech to original speech, the emotion recognition performance is degraded by 15% relative to IEMOCAP data, similar to the degradation observed for automatic speech recognition used to evaluate the preservation of the linguistic information.
|
|
Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO]Computer Science [cs]; Emotion Recognition; Speaker Anonymization; Voice Privacy
|
|
URL: https://hal.inria.fr/hal-03377797
https://hal.inria.fr/hal-03377797/file/ISCA_SPSC_Symposium_EMOTION.pdf
https://hal.inria.fr/hal-03377797/document
|
|
BASE
|
|
Hide details
|
|
|
| 18 |
An investigation into variability conditions in the SRE 2004 and 2008 Corpora ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
