Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Tomashenko Natalia A"'
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 metrics to perform automatic speaker verification based only on phoneme du
Externí odkaz:
http://arxiv.org/abs/2412.17164
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 focuses on developing attacker systems against voice anonymization, which
Externí odkaz:
http://arxiv.org/abs/2410.07428
Autor:
Miao, Xiaoxiao, Zhang, Yuxiang, Wang, Xin, Tomashenko, Natalia, Soh, Donny Cheng Lock, Mcloughlin, Ian
A general disentanglement-based speaker anonymization system typically separates speech into content, speaker, and prosody features using individual encoders. This paper explores how to adapt such a system when a new speech attribute, for example, em
Externí odkaz:
http://arxiv.org/abs/2408.05928
Autor:
Panariello, Michele, Tomashenko, Natalia, Wang, Xin, Miao, Xiaoxiao, Champion, Pierre, Nourtel, Hubert, Todisco, Massimiliano, Evans, Nicholas, Vincent, Emmanuel, Yamagishi, Junichi
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 second edition held in 2022. We describe the voice anonymisation task and d
Externí odkaz:
http://arxiv.org/abs/2407.11516
Autor:
Tomashenko, Natalia, Miao, Xiaoxiao, Champion, Pierre, Meyer, Sarina, Wang, Xin, Vincent, Emmanuel, Panariello, Michele, Evans, Nicholas, Yamagishi, Junichi, Todisco, Massimiliano
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 states. The organizers provide development and evaluation datasets and
Externí odkaz:
http://arxiv.org/abs/2404.02677
Autor:
Parcollet, Titouan, Nguyen, Ha, Evain, Solene, Boito, Marcely Zanon, Pupier, Adrien, Mdhaffar, Salima, Le, Hang, Alisamir, Sina, Tomashenko, Natalia, Dinarelli, Marco, Zhang, Shucong, Allauzen, Alexandre, Coavoux, Maximin, Esteve, Yannick, Rouvier, Mickael, Goulian, Jerome, Lecouteux, Benjamin, Portet, Francois, Rossato, Solange, Ringeval, Fabien, Schwab, Didier, Besacier, Laurent
Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing. Speech processing drastically benefitted from SSL as most of the current domain-related
Externí odkaz:
http://arxiv.org/abs/2309.05472
Speaker anonymization aims to conceal a speaker's identity while preserving content information in speech. Current mainstream neural-network speaker anonymization systems disentangle speech into prosody-related, content, and speaker representations.
Externí odkaz:
http://arxiv.org/abs/2305.18823
Autor:
Nguyen, Tuan, Mdhaffar, Salima, Tomashenko, Natalia, Bonastre, Jean-François, Estève, Yannick
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-LIUM 3 dataset, our experiments show that such a model can obtain, with
Externí odkaz:
http://arxiv.org/abs/2302.10790
Autor:
Tomashenko, Natalia, Srivastava, Brij Mohan Lal, Wang, Xin, Vincent, Emmanuel, Nautsch, Andreas, Yamagishi, Junichi, Evans, Nicholas, Patino, Jose, Bonastre, Jean-François, Noé, Paul-Gauthier, Todisco, Massimiliano
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
Externí odkaz:
http://arxiv.org/abs/2205.07123
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 then used in speech processing downstream tasks such as automatic spee
Externí odkaz:
http://arxiv.org/abs/2204.01397