Search Results for author: Jean-François Bonastre

Found 10 papers, 8 papers with code

Federated Learning for ASR based on Wav2vec 2.0

2 code implementations20 Feb 2023 Tuan Nguyen, Salima Mdhaffar, Natalia Tomashenko, Jean-François Bonastre, Yannick Estève

This paper presents a study on the use of federated learning to train an ASR model based on a wav2vec 2. 0 model pre-trained by self supervision.

Federated Learning Language Modelling

Hiding speaker's sex in speech using zero-evidence speaker representation in an analysis/synthesis pipeline

1 code implementation29 Nov 2022 Paul-Gauthier Noé, Xiaoxiao Miao, Xin Wang, Junichi Yamagishi, Jean-François Bonastre, Driss Matrouf

The use of modern vocoders in an analysis/synthesis pipeline allows us to investigate high-quality voice conversion that can be used for privacy purposes.

Voice Conversion

The VoicePrivacy 2020 Challenge Evaluation Plan

1 code implementation14 May 2022 Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang, Emmanuel Vincent, Andreas Nautsch, Junichi Yamagishi, Nicholas Evans, Jose Patino, Jean-François Bonastre, Paul-Gauthier Noé, Massimiliano Todisco

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.

Benchmarking

The VoicePrivacy 2022 Challenge Evaluation Plan

2 code implementations23 Mar 2022 Natalia Tomashenko, Xin Wang, Xiaoxiao Miao, Hubert Nourtel, Pierre Champion, Massimiliano Todisco, Emmanuel Vincent, Nicholas Evans, Junichi Yamagishi, Jean-François Bonastre

Participants apply their developed anonymization systems, run evaluation scripts and submit objective evaluation results and anonymized speech data to the organizers.

Speaker Verification

Retrieving Speaker Information from Personalized Acoustic Models for Speech Recognition

no code implementations7 Nov 2021 Salima Mdhaffar, Jean-François Bonastre, Marc Tommasi, Natalia Tomashenko, Yannick Estève

The widespread of powerful personal devices capable of collecting voice of their users has opened the opportunity to build speaker adapted speech recognition system (ASR) or to participate to collaborative learning of ASR.

Speaker Verification speech-recognition +1

Privacy attacks for automatic speech recognition acoustic models in a federated learning framework

no code implementations6 Nov 2021 Natalia Tomashenko, Salima Mdhaffar, Marc Tommasi, Yannick Estève, Jean-François Bonastre

This paper investigates methods to effectively retrieve speaker information from the personalized speaker adapted neural network acoustic models (AMs) in automatic speech recognition (ASR).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Adversarial Disentanglement of Speaker Representation for Attribute-Driven Privacy Preservation

1 code implementation8 Dec 2020 Paul-Gauthier Noé, Mohammad Mohammadamini, Driss Matrouf, Titouan Parcollet, Andreas Nautsch, Jean-François Bonastre

In order to allow the user to choose which information to protect, we introduce in this paper the concept of attribute-driven privacy preservation in speaker voice representation.

Attribute Disentanglement +6

Speech Pseudonymisation Assessment Using Voice Similarity Matrices

2 code implementations30 Aug 2020 Paul-Gauthier Noé, Jean-François Bonastre, Driss Matrouf, Natalia Tomashenko, Andreas Nautsch, Nicholas Evans

The proliferation of speech technologies and rising privacy legislation calls for the development of privacy preservation solutions for speech applications.

De-identification Voice Similarity

Introducing the VoicePrivacy Initiative

3 code implementations4 May 2020 Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang, Emmanuel Vincent, Andreas Nautsch, Junichi Yamagishi, Nicholas Evans, Jose Patino, Jean-François Bonastre, Paul-Gauthier Noé, Massimiliano Todisco

The VoicePrivacy initiative 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.

Benchmarking

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