277 citations · 611 across the 30 of their papers we have counts for
35 papers
The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization
Natalia Tomashenko, Xiaoxiao Miao, Pierre Champion +7
We present results and analyses from the third VoicePrivacy Challenge held in 2024, which focuses on advancing voice anonymization technologies. The task was to develop a voice ano…
Target speaker anonymization in multi-speaker recordings
Natalia Tomashenko, Junichi Yamagishi, Xin Wang +2
Most of the existing speaker anonymization research has focused on single-speaker audio, leading to the development of techniques and evaluation metrics optimized for such conditio…
Exploiting Context-dependent Duration Features for Voice Anonymization Attack Systems
Natalia Tomashenko, Emmanuel Vincent, Marc Tommasi
The temporal dynamics of speech, encompassing variations in rhythm, intonation, and speaking rate, contain important and unique information about speaker identity. This paper propo…
The First VoicePrivacy Attacker Challenge
Natalia Tomashenko, Xiaoxiao Miao, Emmanuel Vincent +1
The First VoicePrivacy Attacker Challenge is an ICASSP 2025 SP Grand Challenge which focuses on evaluating attacker systems against a set of voice anonymization systems submitted t…
Analysis of Speech Temporal Dynamics in the Context of Speaker Verification and Voice Anonymization
Natalia Tomashenko, Emmanuel Vincent, Marc Tommasi
In this paper, we investigate the impact of speech temporal dynamics in application to automatic speaker verification and speaker voice anonymization tasks. We propose several metr…
The First VoicePrivacy Attacker Challenge Evaluation Plan
Natalia Tomashenko, Xiaoxiao Miao, Emmanuel Vincent +1
The First VoicePrivacy Attacker Challenge is a new kind of challenge organized as part of the VoicePrivacy initiative and supported by ICASSP 2025 as the SP Grand Challenge It focu…
Adapting General Disentanglement-Based Speaker Anonymization for Enhanced Emotion Preservation
Xiaoxiao Miao, Yuxiang Zhang, Xin Wang +3
A general disentanglement-based speaker anonymization system typically separates speech into content, speaker, and prosody features using individual encoders. This paper explores h…
The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation
Michele Panariello, Natalia Tomashenko, Xin Wang +7
The VoicePrivacy Challenge promotes the development of voice anonymisation solutions for speech technology. In this paper we present a systematic overview and analysis of the secon…
The VoicePrivacy 2024 Challenge Evaluation Plan
Natalia Tomashenko, Xiaoxiao Miao, Pierre Champion +7
The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional s…
LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech
Titouan Parcollet, Ha Nguyen, Solene Evain +19
Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing. Speech processing…
Speaker anonymization using orthogonal Householder neural network
Xiaoxiao Miao, Xin Wang, Erica Cooper +2
Speaker anonymization aims to conceal a speaker's identity while preserving content information in speech. Current mainstream neural-network speaker anonymization systems disentang…
Federated Learning for ASR based on Wav2vec 2.0
Tuan Nguyen, Salima Mdhaffar, Natalia Tomashenko +2
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. Carried out on the well-known TED-L…
The VoicePrivacy 2020 Challenge Evaluation Plan
Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang +8
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…
A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems
Marcely Zanon Boito, Laurent Besacier, Natalia Tomashenko +1
Self-supervised models for speech processing emerged recently as popular foundation blocks in speech processing pipelines. These models are pre-trained on unlabeled audio data and…
Analyzing Language-Independent Speaker Anonymization Framework under Unseen Conditions
Xiaoxiao Miao, Xin Wang, Erica Cooper +2
In our previous work, we proposed a language-independent speaker anonymization system based on self-supervised learning models. Although the system can anonymize speech data of any…
The VoicePrivacy 2022 Challenge Evaluation Plan
Natalia Tomashenko, Xin Wang, Xiaoxiao Miao +7
For new participants - Executive summary: (1) The task is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting lingu…
Language-Independent Speaker Anonymization Approach using Self-Supervised Pre-Trained Models
Xiaoxiao Miao, Xin Wang, Erica Cooper +2
Speaker anonymization aims to protect the privacy of speakers while preserving spoken linguistic information from speech. Current mainstream neural network speaker anonymization sy…
Retrieving Speaker Information from Personalized Acoustic Models for Speech Recognition
Salima Mdhaffar, Jean-François Bonastre, Marc Tommasi +2
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 par…
Privacy attacks for automatic speech recognition acoustic models in a federated learning framework
Natalia Tomashenko, Salima Mdhaffar, Marc Tommasi +2
This paper investigates methods to effectively retrieve speaker information from the personalized speaker adapted neural network acoustic models (AMs) in automatic speech recogniti…
The VoicePrivacy 2020 Challenge: Results and findings
Natalia Tomashenko, Xin Wang, Emmanuel Vincent +11
This paper presents the results and analyses stemming from the first VoicePrivacy 2020 Challenge which focuses on developing anonymization solutions for speech technology. We provi…
Benchmarking and challenges in security and privacy for voice biometrics
Jean-Francois Bonastre, Hector Delgado, Nicholas Evans +13
For many decades, research in speech technologies has focused upon improving reliability. With this now meeting user expectations for a range of diverse applications, speech techno…
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
Solene Evain, Ha Nguyen, Hang Le +15
Self-Supervised Learning (SSL) using huge unlabeled data has been successfully explored for image and natural language processing. Recent works also investigated SSL from speech. T…
Speaker anonymisation using the McAdams coefficient
Jose Patino, Natalia Tomashenko, Massimiliano Todisco +2
Anonymisation has the goal of manipulating speech signals in order to degrade the reliability of automatic approaches to speaker recognition, while preserving other aspects of spee…
Speech Pseudonymisation Assessment Using Voice Similarity Matrices
Paul-Gauthier Noé, Jean-François Bonastre, Driss Matrouf +3
The proliferation of speech technologies and rising privacy legislation calls for the development of privacy preservation solutions for speech applications. These are essential sin…