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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)
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2
The VoicePrivacy 2022 Challenge Evaluation Plan ...
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Automatic Classification of Phonation Types in Spontaneous Speech: Towards a New Workflow for the Characterization of Speakers’ Voice Quality
In: Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03334492 ; Interspeech 2021, Aug 2021, Brno, Czech Republic. pp.1015-1018, ⟨10.21437/Interspeech.2021-1765⟩ (2021)
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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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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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6
The VoicePrivacy 2020 Challenge: Results and findings
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
In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021)
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8
The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
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9
Benchmarking and challenges in security and privacy for voice biometrics
In: SPSC 2021, 1st ISCA Symposium on Security and Privacy in Speech Communication ; https://hal.archives-ouvertes.fr/hal-03346196 ; SPSC 2021, 1st ISCA Symposium on Security and Privacy in Speech Communication, ISCA, Nov 2021, Magdeburg, Germany. ⟨10.21437/SPSC.2021-11⟩ ; https://spsc-symposium2021.de/#home (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
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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12
The VoicePrivacy 2020 Challenge: Results and findings
In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021)
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13
Discriminating speakers using perceptual clustering interface
In: XVII AISV Conference: Speaker Individuality in Phonetics and Speech Sciences: Speech Technology and Forensic Applications ; https://hal.archives-ouvertes.fr/hal-03160943 ; XVII AISV Conference: Speaker Individuality in Phonetics and Speech Sciences: Speech Technology and Forensic Applications, Feb 2021, Zurich, Switzerland (2021)
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14
Anonymous speaker clusters: Making distinctions between anonymised speech recordings with clustering interface
In: INTERSPEECH 2021 ; https://hal.archives-ouvertes.fr/hal-03267084 ; INTERSPEECH 2021, Aug 2021, Brno, Czech Republic (2021)
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15
Anonymous speaker clusters: Making distinctions between anonymised speech recordings with clustering interface
In: INTERSPEECH 2021 ; https://hal.archives-ouvertes.fr/hal-03267084 ; INTERSPEECH 2021, Aug 2021, Brno, Czech Republic (2021)
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16
Introducing the VoicePrivacy initiative
In: INTERSPEECH 2020 ; https://hal.inria.fr/hal-02562199 ; INTERSPEECH 2020, Oct 2020, Shanghai, China (2020)
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17
The VoicePrivacy 2020 Challenge Evaluation Plan
In: https://hal.archives-ouvertes.fr/hal-03623450 ; [Other] LIA - Laboratoire Informatique d'Avignon; MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery; Eurecom [Sophia Antipolis]; University of Edinburgh. 2020 (2020)
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18
Speech Pseudonymisation Assessment Using Voice Similarity Matrices ...
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19
Acoustic Pairing of Original and Dubbed Voices in the Context of Video Game Localization
In: Interspeech ; https://hal.archives-ouvertes.fr/hal-01572151 ; Interspeech, Aug 2017, Stockholm, Sweden. pp.2839-2843, ⟨10.21437/Interspeech.2017-1311⟩ ; http://www.isca-speech.org/archive/Interspeech_2017/abstracts/1311.html (2017)
Abstract: International audience ; The aim of this research work is the development of an automatic voice recommendation system for assisted voice casting. In this article, we propose preliminary work on acoustic pair-ing of original and dubbed voices. The voice segments are taken from a video game released in two different languages. The paired voice segments come from different languages but belong to the same video game character. Our wish is to exploit the relationship between a set of paired segments in order to model the perceptual aspects of a given character depending on the target language. We use a state-of-the-art approach in speaker recognition (i.e. based on the paradigm i-vector/PLDA). We first evaluate pairs of i-vectors using two different acoustic spaces, one for each of the targeted languages. Secondly, we perform a transformation in order to project the source-language i-vector into the target language. The results showed that this latest approach is able to improve significantly the accuracy. Finally, we challenge the system ability to model the latent information that holds the video-game character independently of the speaker, the linguistic content and the language .
Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD]; i-vector; Speaker recognition; Video game; Voice casting; Voice similarity
URL: https://doi.org/10.21437/Interspeech.2017-1311
https://hal.archives-ouvertes.fr/hal-01572151/file/confusion.pdf
https://hal.archives-ouvertes.fr/hal-01572151/document
https://hal.archives-ouvertes.fr/hal-01572151/file/confusion_matrix.pdf
https://hal.archives-ouvertes.fr/hal-01572151
https://hal.archives-ouvertes.fr/hal-01572151/file/results.pdf
https://hal.archives-ouvertes.fr/hal-01572151/file/main.pdf
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20
Forensic speaker recognition : mirages and reality
In: Individual differences in speech production and perception (Frankfurt am Main, 2015), p. 255-284
MPI für Psycholinguistik
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