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The VoicePrivacy 2020 Challenge Evaluation Plan

Natalia Tomashenko 1 Brij Mohan Lal Srivastava 2, 3 Xin Wang 4 Emmanuel Vincent 3 Andreas Nautsch 5 Junichi yamagishi 4 Nicholas Evans 5 Jose Patino 5 Jean-François Bonastre 1 Paul-Gauthier Noé 1 Massimiliano Todisco 5 
2 MAGNET - Machine Learning in Information Networks
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : The VoicePrivacy Challenge aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of challenges. In this document, we formulate the voice anonymization task selected for the VoicePrivacy 2020 Challenge and describe the datasets used for system development and evaluation. We also present the attack models and the associated objective and subjective evaluation metrics. We introduce two anonymization baselines and report objective evaluation results.
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https://hal.archives-ouvertes.fr/hal-03623450
Contributor : Natalia Tomashenko Connect in order to contact the contributor
Submitted on : Tuesday, May 17, 2022 - 3:07:49 AM
Last modification on : Monday, May 23, 2022 - 3:26:02 AM

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  • HAL Id : hal-03623450, version 2

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Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang, Emmanuel Vincent, Andreas Nautsch, et al.. The VoicePrivacy 2020 Challenge Evaluation Plan. [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. ⟨hal-03623450v2⟩

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