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Conference papers

Anonymisation de parole par quantification vectorielle

Pierre Champion 1 Denis Jouvet 1 Anthony Larcher 2 
1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : With the popularity of virtual assistants (e.g., Siri, Alexa), the use of speech recognition is now becoming more and more widespread.However, speech signals contain a lot of sensitive information, such as the speaker's identity, which raises privacy concerns.The presented experiments show that the representations extracted by the deep layers of speech recognition networks contain speaker information.This paper aims to produce an anonymous representation while preserving speech recognition performance.To this end, we propose to use vector quantization to constrain the representation space and induce the network to suppress the speaker identity.The choice of the quantization dictionary size allows to configure the trade-off between utility (speech recognition) and privacy (speaker identity concealment).
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Contributor : Pierre CHAMPION Connect in order to contact the contributor
Submitted on : Tuesday, March 15, 2022 - 2:04:12 PM
Last modification on : Wednesday, April 13, 2022 - 10:08:21 AM


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  • HAL Id : hal-03609205, version 1
  • ARXIV : 2203.09518


Pierre Champion, Denis Jouvet, Anthony Larcher. Anonymisation de parole par quantification vectorielle. JEP 2022 - Journées d'Études sur la Parole, Jun 2022, Île de Noirmoutier, France. ⟨hal-03609205⟩



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