Please visit the challenge website for more information about the Challenge.
git clone --recurse-submodules https://github.com/Voice-Privacy-Challenge/Voice-Privacy-Challenge-2020.git- ./install.sh
The recipe uses the pre-trained models of anonymization. To run the baseline system with evaluation:
cd baseline- run
./run.sh. In run.sh, to download models and data the user will be requested the password which is provided during the Challenge registration.
For more details about the baseline and data, please see The VoicePrivacy 2020 Challenge Evaluation Plan
For the latest updates in the baseline and evaluation scripts, please visit News and updates page
The deadline to submit results has passed. To access the baseline models and development/evaluation data, please send an email to [email protected] with “VoicePrivacy-2020" as the subject line. The mail body should include: (i) the name of the contact person; (ii) country; (iii) status (academic/nonacademic).
The dataset for anonymization system traing consists of subsets from the following corpora*:
- LibriSpeech - train-clean-100, train-other-500
- LibriTTS - train-clean-100, train-other-500
- VoxCeleb 1 & 2 - all
*only specified subsets of these corpora can be used for training.
- VCTK - subsets vctk_dev and vctk_test are download from server in run.sh
- LibriSpeech - subsets libri_dev and libri_test are download from server in run.sh
This is the primary (default) baseline.
The baseline system uses several independent models:
- ASR acoustic model to extract BN features (
1_asr_am) - trained on LibriSpeech-train-clean-100 and LibriSpeech-train-other-500 - X-vector extractor (
2_xvect_extr) - trained on VoxCeleb 1 & 2. - Speech synthesis (SS) acoustic model (
3_ss_am) - trained on LibriTTS-train-clean-100. - Neural source filter (NSF) model (
4_nsf) - trained on LibriTTS-train-clean-100.
All the pretrained models are provided as part of this baseline (downloaded by ./baseline/local/download_models.sh)
This is an additional baseline.
To run: ./run.sh --mcadams true
It does not require any training data and is based upon simple signal processing techniques using the McAdams coefficient.
The result file with all the metrics and all datasets for submission will be generated in: ./baseline/exp/results-date-time/results.txt
Please see
for the evalation and development data sets.
- Jean-François Bonastre - University of Avignon - LIA, France
- Nicholas Evans - EURECOM, France
- Fuming Fang - NII, Japan
- Andreas Nautsch - EURECOM, France
- Paul-Gauthier Noé - University of Avignon - LIA, France
- Jose Patino - EURECOM, France
- Md Sahidullah - Inria, France
- Brij Mohan Lal Srivastava - Inria, France
- Natalia Tomashenko - University of Avignon - LIA, France
- Massimiliano Todisco - EURECOM, France
- Emmanuel Vincent - Inria, France
- Xin Wang - NII, Japan
- Junichi Yamagishi - NII, Japan and University of Edinburgh, UK
Contact: [email protected]
This work was supported in part by the French National Research Agency under projects HARPOCRATES (ANR-19-DATA-0008) and DEEP-PRIVACY (ANR-18- CE23-0018), by the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No. 825081 COMPRISE (https://www.compriseh2020.eu/), and jointly by the French National Research Agency and the Japan Science and Technology Agency under project VoicePersonae.
Copyright (C) 2020
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.
@inproceedings{tomashenko2020introducing,
author={N. Tomashenko and Brij Mohan Lal Srivastava and Xin Wang and Emmanuel Vincent and Andreas Nautsch and Junichi Yamagishi and Nicholas Evans and Jose Patino and Jean-François Bonastre and Paul-Gauthier Noé and Massimiliano Todisco},
title={{Introducing the VoicePrivacy Initiative}},
year=2020,
booktitle={Proc. Interspeech 2020},
pages={1693--1697},
doi={10.21437/Interspeech.2020-1333},
url={http://dx.doi.org/10.21437/Interspeech.2020-1333}
}
@article{tomashenko2022voiceprivacy,
title={The VoicePrivacy 2020 Challenge: Results and findings},
author={Tomashenko, Natalia and Wang, Xin and Vincent, Emmanuel and Patino, Jose and Srivastava, Brij Mohan Lal and No{\'e}, Paul-Gauthier and Nautsch, Andreas and Evans, Nicholas and Yamagishi, Junichi and O’Brien, Benjamin and others},
journal={Computer Speech \& Language},
volume={74},
pages={101362},
year={2022},
publisher={Elsevier},
url={https://doi.org/10.1016/j.csl.2022.101362}
}
article{tomashenkovoiceprivacy,
title={The {VoicePrivacy} 2020 {Challenge} Evaluation Plan},
author={Tomashenko, Natalia and Srivastava, Brij Mohan Lal and Wang, Xin and Vincent, Emmanuel and Nautsch, Andreas and Yamagishi, Junichi and Evans, Nicholas and Patino, Jose and Bonastre, Jean-Fran{\c{c}}ois and No{\'e}, Paul-Gauthier and Todisco, Massimiliano},
url={https://www.voiceprivacychallenge.org/docs/VoicePrivacy_2020_Eval_Plan_v1_3.pdf},
year={2020}
}
For the post-evaluation analysis novel anonymization metrics have been integrated to the baseline evaluation:
- The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment: https://arxiv.org/pdf/2005.09413.pdf
- Speech Pseudonymisation Assessment Using Voice Similarity Matrices: https://arxiv.org/pdf/2008.13144.pdf