Obserwuj
Natalia Tomashenko
Tytuł
Cytowane przez
Cytowane przez
Rok
TED-LIUM 3: Twice as much data and corpus repartition for experiments on speaker adaptation
F Hernandez, V Nguyen, S Ghannay, N Tomashenko, Y Esteve
Speech and Computer: 20th International Conference, SPECOM 2018, Leipzig …, 2018
3852018
Introducing the VoicePrivacy initiative
N Tomashenko, BML Srivastava, X Wang, E Vincent, A Nautsch, ...
arXiv preprint arXiv:2005.01387, 2020
1382020
The voiceprivacy 2020 challenge: Results and findings
N Tomashenko, X Wang, E Vincent, J Patino, BML Srivastava, PG Noé, ...
Computer Speech & Language 74, 101362, 2022
1262022
Speaker anonymisation using the McAdams coefficient
J Patino, N Tomashenko, M Todisco, A Nautsch, N Evans
arXiv preprint arXiv:2011.01130, 2020
1112020
The VoicePrivacy 2024 Challenge Evaluation Plan
N Tomashenko, X Miao, P Champion, S Meyer, X Wang, E Vincent, ...
arXiv preprint arXiv:2404.02677, 2024
1052024
Lebenchmark: A reproducible framework for assessing self-supervised representation learning from speech
S Evain, H Nguyen, H Le, MZ Boito, S Mdhaffar, S Alisamir, Z Tong, ...
arXiv preprint arXiv:2104.11462, 2021
932021
Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability
A Caubrière, N Tomashenko, A Laurent, E Morin, N Camelin, Y Esteve
arXiv preprint arXiv:1906.07601, 2019
732019
Design choices for x-vector based speaker anonymization
BML Srivastava, N Tomashenko, X Wang, E Vincent, J Yamagishi, ...
arXiv preprint arXiv:2005.08601, 2020
672020
Privacy and utility of x-vector based speaker anonymization
BML Srivastava, M Maouche, M Sahidullah, E Vincent, A Bellet, ...
IEEE/ACM Transactions on Audio, Speech, and Language Processing 30, 2383-2395, 2022
482022
Investigating Self-supervised Pre-training for End-to-end Speech Translation
H Nguyen, F Bougares, N Tomashenko, Y Estève
472020
The VoicePrivacy 2020 challenge evaluation plan
N Tomashenko, BML Srivastava, X Wang, E Vincent, A Nautsch, ...
LIA-Laboratoire Informatique d'Avignon; MULTISPEECH-Speech Modeling for …, 2020
44*2020
Speaker adaptation of context dependent deep neural networks based on MAP-adaptation and GMM-derived feature processing.
NA Tomashenko, YY Khokhlov
Interspeech, 2997-3001, 2014
422014
Task agnostic and task specific self-supervised learning from speech with lebenchmark
S Evain, MH Nguyen, H Le, MZ Boito, S Mdhaffar, S Alisamir, Z Tong, ...
Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), 2021
392021
Speech pseudonymisation assessment using voice similarity matrices
PG Noé, JF Bonastre, D Matrouf, N Tomashenko, A Nautsch, N Evans
arXiv preprint arXiv:2008.13144, 2020
392020
Investigating adaptation and transfer learning for end-to-end spoken language understanding from speech
N Tomashenko, A Caubriere, Y Estève
Interspeech 2019, 824-828, 2019
382019
The privacy ZEBRA: Zero evidence biometric recognition assessment
A Nautsch, J Patino, N Tomashenko, J Yamagishi, PG Noé, JF Bonastre, ...
arXiv preprint arXiv:2005.09413, 2020
352020
An Investigation of Mixup Training Strategies for Acoustic Models in ASR.
I Medennikov, YY Khokhlov, A Romanenko, D Popov, NA Tomashenko, ...
Interspeech, 2903-2907, 2018
322018
Privacy attacks for automatic speech recognition acoustic models in a federated learning framework
N Tomashenko, S Mdhaffar, M Tommasi, Y Estève, JF Bonastre
ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022
312022
Recent advances in end-to-end spoken language understanding
N Tomashenko, A Caubrière, Y Estève, A Laurent, E Morin
Statistical Language and Speech Processing: 7th International Conference …, 2019
302019
Language-independent speaker anonymization approach using self-supervised pre-trained models
X Miao, X Wang, E Cooper, J Yamagishi, N Tomashenko
arXiv preprint arXiv:2202.13097, 2022
272022
Nie można teraz wykonać tej operacji. Spróbuj ponownie później.
Prace 1–20