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Speaker information modification in the VoicePrivacy 2020 toolchain

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 : This paper presents a study of the baseline system of the VoicePrivacy 2020 challenge. This baseline relies on a voice conversion system that aims at separating speaker identity and linguistic contents for a given speech utterance. To generate an anonymized speech waveform, the neural acoustic model and neural waveform model use the related linguistic content together with a selected pseudo-speaker identity. The linguistic content is estimated using bottleneck features extracted from a triphone classifier while the speaker information is extracted then modified to target a pseudo-speaker identity in the x-vector's space. In this work, we first proposed to replace the triphone-based bottleneck features extractor that requires supervised training by an end-to-end Automatic Speech Recognition (ASR) system. In this framework, we explored the use of adver-sarial and semi-adversarial training to learn linguistic features while masking speaker information. Last, we explored several anonymization schemes to introspect which module benefits the most from the generated pseudo-speaker identities.
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Submitted on : Monday, November 9, 2020 - 1:20:52 PM
Last modification on : Saturday, June 25, 2022 - 7:40:40 PM
Long-term archiving on: : Wednesday, February 10, 2021 - 6:58:07 PM


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  • HAL Id : hal-02995855, version 1


Pierre Champion, Denis Jouvet, Anthony Larcher. Speaker information modification in the VoicePrivacy 2020 toolchain. [Research Report] INRIA Nancy, équipe Multispeech; LIUM - Laboratoire d'Informatique de l'Université du Mans. 2020. ⟨hal-02995855⟩



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