This repository provides a python implementation of Invertibility of a Voice Privacy System using Embedding Alignment, which enables unsupervised speaker-de-anonymization conversion using Wasserstein-Procrustes and Procrustes. The proposed method can recover up to 62% of the speaker identities from anonymized x-vectors.
@inproceedings{vpc_invertibility,
TITLE = {{On the invertibility of a voice privacy system using embedding alignement}},
AUTHOR = {Champion, Pierre and Thebaud, Thomas and Le Lan, Ga{\"e}l and Larcher, Anthony and Jouvet, Denis},
URL = {https://hal.archives-ouvertes.fr/hal-03356021},
BOOKTITLE = {{ASRU 2021 - IEEE Automatic Speech Recognition and Understanding Workshop}},
ADDRESS = {Cartagena, Colombia},
YEAR = {2021},
}
./install.shVoicePrivacy linkability Scores without retrained ASV on anon data:
./run.sh --stage 0 --skip-stage "1 2 3 4"results (Clickable 🖱️):
Reproduce VoicePrivacy EER results with cosine scoring
**ASV (original): test_trials_f original <=> test_enrolls - original**
ROC_EER: 10.33
EER: 10.13
Cllr (min/act): 0.354056 0.872250
linkability: 0.768135
**ASV (original): test_trials_m original <=> test_enrolls - original**
ROC_EER: 2.85
EER: 2.72
Cllr (min/act): 0.087939 0.838062
linkability: 0.910283
**ASV (original): test_trials_f anonymized <=> test_enrolls - original**
ROC_EER: 48.99
EER: 49.36
Cllr (min/act): 0.999199 1.022628
linkability: 0.068093
**ASV (original): test_trials_m anonymized <=> test_enrolls - original**
ROC_EER: 42.64
EER: 49.29
Cllr (min/act): 0.996365 1.033778
linkability: 0.148108
**ASV (original): test_trials_f anonymized <=> test_enrolls - anonymized**
ROC_EER: 29.41
EER: 29.03
Cllr (min/act): 0.801984 1.106415
linkability: 0.327953
**ASV (original): test_trials_m anonymized <=> test_enrolls - anonymized**
ROC_EER: 29.15
EER: 28.87
Cllr (min/act): 0.816143 1.110715
linkability: 0.303574
Spk verif scores:
ASV-libri_test_enrolls_anon-libri_test_trials_f_anon
EER: 31.39%
Cllr (min/act): 0.830/15.787
ROCCH-EER: 30.943%
ASV-libri_test_enrolls_anon-libri_test_trials_m_anon
EER: 36.75%
Cllr (min/act): 0.909/34.970
ROCCH-EER: 36.281%
with retrained x-vector:
ASV-libri_test_enrolls_anon-libri_test_trials_f_anon
EER: 31.39%
Cllr (min/act): 0.830/15.787
ROCCH-EER: 30.943%
ASV-libri_test_enrolls_anon-libri_test_trials_m_anon
EER: 36.75%
Cllr (min/act): 0.909/34.970
ROCCH-EER: 36.281%
---VoicePrivacy linkability Scores with retrained ASV on anon data:
./run.sh --stage 0 --skip-stage "1 2 3 4" --retrained-anon-xtractor trueresults:
Reproduce VoicePrivacy EER results with cosine scoring
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV (anon): test_trials_f anonymized <=> test_enrolls - anonymized**
ROC_EER: 17.12
EER: 16.90
Cllr (min/act): 0.513022 1.076549
linkability: 0.575353
**ASV (anon): test_trials_m anonymized <=> test_enrolls - anonymized**
ROC_EER: 14.08
EER: 13.59
Cllr (min/act): 0.487963 1.084177
linkability: 0.608591
Spk verif scores:
ASV-libri_test_enrolls_anon-libri_test_trials_f_anon
EER: 31.39%
Cllr (min/act): 0.830/15.787
ROCCH-EER: 30.943%
ASV-libri_test_enrolls_anon-libri_test_trials_m_anon
EER: 36.75%
Cllr (min/act): 0.909/34.970
ROCCH-EER: 36.281%
with retrained x-vector:
ASV-libri_test_enrolls_anon-libri_test_trials_f_anon
EER: 11.5%
Cllr (min/act): 0.364/2.832
ROCCH-EER: 11.278%
ASV-libri_test_enrolls_anon-libri_test_trials_m_anon
EER: 9.354%
Cllr (min/act): 0.305/3.941
ROCCH-EER: 9.171%
---Procrustes linkability and invertibility Scores x-tractor original:
./run.sh --stage 1results:
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
== Training rotation ==
Procrustes rotation estimation
Compute done, rotation shape : (512, 512)
Top 1: 96.12 (speaker accuracy) 34.47 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Top 1: 25.48 (speaker accuracy) 1.36 (segment accuracy)
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Top 1: 36.61 (speaker accuracy) 2.36 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 41.94
EER: 39.37
Cllr (min/act): 0.889367 1.009684
linkability: 0.199870
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 30.55
EER: 30.47
Cllr (min/act): 0.834911 0.999177
linkability: 0.310689Procrustes linkability and invertibility Scores x-tractor original gender specific training:
./run.sh --stage 1 --filter_gender f
./run.sh --stage 1 --filter_gender mresults (merged):
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender f
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 254
x_vector_u samples after filtering: 254
== Training rotation ==
Procrustes rotation estimation
Compute done, rotation shape : (512, 512)
Top 1: 96.46 (speaker accuracy) 47.64 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Top 1: 26.57 (speaker accuracy) 1.77 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 40.12
EER: 37.58
Cllr (min/act): 0.875361 1.009219
linkability: 0.218478
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender m
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 184
x_vector_u samples after filtering: 184
== Training rotation ==
Procrustes rotation estimation
Compute done, rotation shape : (512, 512)
Top 1: 97.28 (speaker accuracy) 54.35 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Top 1: 45.80 (speaker accuracy) 2.36 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 30.96
EER: 30.75
Cllr (min/act): 0.825736 1.003011
linkability: 0.288890Procrustes linkability and invertibility Scores x-tractor original with pca on x-vector:
./run.sh --stage 1 --frontend-train "--pca --pca_n_dim 70"results:
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (438, 70) with a total explained variance ratio on clear data: 0.98974174
Output shape after PCA: (438, 70) total explained variance ratio on target(anonymized) data: 0.98620886
Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 91.32 (speaker accuracy) 23.06 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Loading pca from: exp/enroll_train_wp
Top 1: 27.52 (speaker accuracy) 1.77 (segment accuracy)
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Loading pca from: exp/enroll_train_wp
Top 1: 40.29 (speaker accuracy) 2.49 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 32.64
EER: 32.32
Cllr (min/act): 0.802954 0.962672
linkability: 0.275204
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 32.72
EER: 32.43
Cllr (min/act): 0.848538 0.998714
linkability: 0.278785Procrustes linkability and invertibility Scores x-tractor original gender specific training with pca on x-vector:
./run.sh --stage 1 --filter_gender f --frontend-train "--pca --pca_n_dim 70"
./run.sh --stage 1 --filter_gender m --frontend-train "--pca --pca_n_dim 70"results (merged):
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender f
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 254
x_vector_u samples after filtering: 254
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (254, 70) with a total explained variance ratio on clear data: 0.9914968
Output shape after PCA: (254, 70) total explained variance ratio on target(anonymized) data: 0.9798316
Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 94.49 (speaker accuracy) 40.16 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Loading pca from: exp/enroll_train_wp
Top 1: 30.52 (speaker accuracy) 1.91 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 25.33
EER: 24.67
Cllr (min/act): 0.708871 0.919555
linkability: 0.414599
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender m
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 184
x_vector_u samples after filtering: 184
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (184, 70) with a total explained variance ratio on clear data: 0.9933948
Output shape after PCA: (184, 70) total explained variance ratio on target(anonymized) data: 0.9853459
Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 97.83 (speaker accuracy) 48.91 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Loading pca from: exp/enroll_train_wp
Top 1: 50.39 (speaker accuracy) 4.20 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 24.46
EER: 24.27
Cllr (min/act): 0.687435 0.905601
linkability: 0.443180Procrustes linkability and invertibility Scores x-tractor anon:
./run.sh --stage 1 --retrained-anon-xtractor trueresults:
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
== Training rotation ==
Procrustes rotation estimation
Compute done, rotation shape : (512, 512)
Top 1: 97.95 (speaker accuracy) 30.59 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Top 1: 58.72 (speaker accuracy) 3.27 (segment accuracy)
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Top 1: 59.19 (speaker accuracy) 2.89 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 28.99
EER: 28.75
Cllr (min/act): 0.756053 0.987522
linkability: 0.374424
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 23.65
EER: 23.10
Cllr (min/act): 0.663404 0.977133
linkability: 0.457706Procrustes linkability and invertibility Scores x-tractor anon gender specific training:
./run.sh --stage 1 --retrained-anon-xtractor true --filter_gender f
./run.sh --stage 1 --retrained-anon-xtractor true --filter_gender mresults (merged):
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender f
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 254
x_vector_u samples after filtering: 254
== Training rotation ==
Procrustes rotation estimation
Compute done, rotation shape : (512, 512)
Top 1: 99.21 (speaker accuracy) 53.54 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Top 1: 54.77 (speaker accuracy) 3.27 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 27.01
EER: 26.56
Cllr (min/act): 0.704388 0.984556
linkability: 0.425735
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender m
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 184
x_vector_u samples after filtering: 184
== Training rotation ==
Procrustes rotation estimation
Compute done, rotation shape : (512, 512)
Top 1: 99.46 (speaker accuracy) 53.26 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Top 1: 56.56 (speaker accuracy) 3.02 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 21.15
EER: 21.15
Cllr (min/act): 0.612003 0.976945
linkability: 0.481244Procrustes linkability and invertibility Scores x-tractor anon with pca on x-vector:
./run.sh --stage 1 --retrained-anon-xtractor true --frontend-train "--pca --pca_n_dim 70"results:
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (438, 70) with a total explained variance ratio on clear data: 0.9897418
Output shape after PCA: (438, 70) total explained variance ratio on target(anonymized) data: 0.98574746
Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 95.43 (speaker accuracy) 21.46 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Loading pca from: exp/enroll_train_wp
Top 1: 52.45 (speaker accuracy) 2.86 (segment accuracy)
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Loading pca from: exp/enroll_train_wp
Top 1: 57.48 (speaker accuracy) 2.49 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 21.55
EER: 21.21
Cllr (min/act): 0.599200 0.937384
linkability: 0.491354
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 23.16
EER: 22.98
Cllr (min/act): 0.669663 0.968386
linkability: 0.456137Procrustes linkability and invertibility Scores x-tractor anon gender specific training and pca on x-vector:
./run.sh --stage 1 --retrained-anon-xtractor true --filter_gender f --frontend-train "--pca --pca_n_dim 70"
./run.sh --stage 1 --retrained-anon-xtractor true --filter_gender m --frontend-train "--pca --pca_n_dim 70"results (merged):
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender f
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 254
x_vector_u samples after filtering: 254
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (254, 70) with a total explained variance ratio on clear data: 0.99149674
Output shape after PCA: (254, 70) total explained variance ratio on target(anonymized) data: 0.97646797
Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 99.21 (speaker accuracy) 42.52 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Loading pca from: exp/enroll_train_wp
Top 1: 60.22 (speaker accuracy) 3.27 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 14.62
EER: 14.48
Cllr (min/act): 0.457237 0.864921
linkability: 0.653441
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender m
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 184
x_vector_u samples after filtering: 184
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (184, 70) with a total explained variance ratio on clear data: 0.99339336
Output shape after PCA: (184, 70) total explained variance ratio on target(anonymized) data: 0.9861342
Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 100.00 (speaker accuracy) 51.09 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Loading pca from: exp/enroll_train_wp
Top 1: 59.58 (speaker accuracy) 3.02 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 13.12
EER: 13.11
Cllr (min/act): 0.453624 0.846025
linkability: 0.648048Wasserstein Procrustes linkability and invertibility Scores x-tractor original:
./run.sh --stage 1 --wp trueresults:
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
== Training rotation ==
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (512, 512)
Top 1: 91.55 (speaker accuracy) 15.53 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Top 1: 26.02 (speaker accuracy) 1.36 (segment accuracy)
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Top 1: 25.85 (speaker accuracy) 1.57 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 43.96
EER: 40.78
Cllr (min/act): 0.917082 1.013459
linkability: 0.169659
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 33.90
EER: 33.32
Cllr (min/act): 0.881784 1.005720
linkability: 0.260842Wasserstein Procrustes linkability and invertibility Scores x-tractor original gender specific training:
./run.sh --stage 1 --filter_gender f --wp true
./run.sh --stage 1 --filter_gender m --wp trueresults (merged):
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender f
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 254
x_vector_u samples after filtering: 254
== Training rotation ==
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (512, 512)
Top 1: 98.43 (speaker accuracy) 20.47 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Top 1: 25.61 (speaker accuracy) 1.50 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 40.81
EER: 39.93
Cllr (min/act): 0.919463 1.014105
linkability: 0.166534
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender m
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 184
x_vector_u samples after filtering: 184
== Training rotation ==
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (512, 512)
Top 1: 97.83 (speaker accuracy) 19.02 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Top 1: 21.26 (speaker accuracy) 0.66 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 35.24
EER: 35.01
Cllr (min/act): 0.881113 1.009955
linkability: 0.226058Wasserstein Procrustes linkability and invertibility Scores x-tractor original with pca on x-vector:
./run.sh --stage 1 --frontend-train "--pca --pca_n_dim 70" --wp trueresults:
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (438, 70) with a total explained variance ratio on clear data: 0.9897431
Output shape after PCA: (438, 70) total explained variance ratio on target(anonymized) data: 0.9862072
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 87.44 (speaker accuracy) 12.79 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Loading pca from: exp/enroll_train_wp
Top 1: 19.62 (speaker accuracy) 0.95 (segment accuracy)
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Loading pca from: exp/enroll_train_wp
Top 1: 42.65 (speaker accuracy) 1.97 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 37.47
EER: 36.18
Cllr (min/act): 0.861870 0.971982
linkability: 0.217861
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 37.68
EER: 36.33
Cllr (min/act): 0.892842 1.005039
linkability: 0.216760Wasserstein Procrustes linkability and invertibility Scores x-tractor original gender specific training with pca on x-vector:
./run.sh --stage 1 --filter_gender f --frontend-train "--pca --pca_n_dim 70" --wp true
./run.sh --stage 1 --filter_gender m --frontend-train "--pca --pca_n_dim 70" --wp trueresults (merged):
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender f
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 254
x_vector_u samples after filtering: 254
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (254, 70) with a total explained variance ratio on clear data: 0.9914963
Output shape after PCA: (254, 70) total explained variance ratio on target(anonymized) data: 0.9798459
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 94.49 (speaker accuracy) 23.23 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Loading pca from: exp/enroll_train_wp
Top 1: 26.16 (speaker accuracy) 1.36 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 28.15
EER: 27.41
Cllr (min/act): 0.745610 0.925965
linkability: 0.369201
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender m
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 184
x_vector_u samples after filtering: 184
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (184, 70) with a total explained variance ratio on clear data: 0.993396
Output shape after PCA: (184, 70) total explained variance ratio on target(anonymized) data: 0.98534644
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 98.37 (speaker accuracy) 25.00 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Loading pca from: exp/enroll_train_wp
Top 1: 50.52 (speaker accuracy) 2.23 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 26.55
EER: 26.55
Cllr (min/act): 0.749897 0.915703
linkability: 0.376375Wasserstein Procrustes linkability and invertibility Scores x-tractor anon:
./run.sh --stage 1 --wp true --retrained-anon-xtractor trueresults:
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
== Training rotation ==
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (512, 512)
Top 1: 99.32 (speaker accuracy) 14.38 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Top 1: 56.54 (speaker accuracy) 2.45 (segment accuracy)
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Top 1: 57.48 (speaker accuracy) 2.89 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 31.35
EER: 30.79
Cllr (min/act): 0.812413 0.992908
linkability: 0.325512
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 25.62
EER: 24.64
Cllr (min/act): 0.689764 0.980588
linkability: 0.442820Wasserstein Procrustes linkability and invertibility Scores x-tractor anon gender specific training:
./run.sh --stage 1 --filter_gender f --wp true --retrained-anon-xtractor true
./run.sh --stage 1 --filter_gender m --wp true --retrained-anon-xtractor trueresults (merged):
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender f
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 254
x_vector_u samples after filtering: 254
== Training rotation ==
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (512, 512)
Top 1: 99.21 (speaker accuracy) 28.74 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Top 1: 51.50 (speaker accuracy) 3.27 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 29.17
EER: 28.61
Cllr (min/act): 0.736770 0.988891
linkability: 0.372278
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender m
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 184
x_vector_u samples after filtering: 184
== Training rotation ==
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (512, 512)
Top 1: 99.46 (speaker accuracy) 18.48 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Top 1: 50.26 (speaker accuracy) 2.49 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
ROC_EER: 24.81
EER: 24.00
Cllr (min/act): 0.681331 0.983677
linkability: 0.435266Wasserstein Procrustes linkability and invertibility Scores x-tractor anon with pca on x-vector:
./run.sh --stage 1 --frontend-train "--pca --pca_n_dim 70" --wp true --retrained-anon-xtractor trueresults:
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (438, 70) with a total explained variance ratio on clear data: 0.98974335
Output shape after PCA: (438, 70) total explained variance ratio on target(anonymized) data: 0.9857518
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 94.98 (speaker accuracy) 14.16 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Loading pca from: exp/enroll_train_wp
Top 1: 44.82 (speaker accuracy) 2.18 (segment accuracy)
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Loading pca from: exp/enroll_train_wp
Top 1: 58.66 (speaker accuracy) 2.36 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 24.23
EER: 24.06
Cllr (min/act): 0.670981 0.947002
linkability: 0.431526
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 25.22
EER: 24.40
Cllr (min/act): 0.697781 0.972370
linkability: 0.418139Wasserstein Procrustes linkability and invertibility Scores x-tractor anon gender specific training with pca on x-vector:
./run.sh --stage 1 --filter_gender f --frontend-train "--pca --pca_n_dim 70" --wp true --retrained-anon-xtractor true
./run.sh --stage 1 --filter_gender m --frontend-train "--pca --pca_n_dim 70" --wp true --retrained-anon-xtractor trueresults (merged):
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender f
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 254
x_vector_u samples after filtering: 254
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (254, 70) with a total explained variance ratio on clear data: 0.99149716
Output shape after PCA: (254, 70) total explained variance ratio on target(anonymized) data: 0.9764552
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 98.43 (speaker accuracy) 24.02 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Loading pca from: exp/enroll_train_wp
Top 1: 57.22 (speaker accuracy) 2.86 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 15.36
EER: 14.80
Cllr (min/act): 0.468355 0.869226
linkability: 0.641547
== Data used to train rotation ==
- xvect_libri_test_enrolls
- xvect_libri_test_enrolls_anon
Filtering by gender m
x_vector_l samples: 438
x_vector_u samples: 438
x_vector_l samples after filtering: 184
x_vector_u samples after filtering: 184
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (184, 70) with a total explained variance ratio on clear data: 0.9933967
Output shape after PCA: (184, 70) total explained variance ratio on target(anonymized) data: 0.98611456
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 99.46 (speaker accuracy) 38.59 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Loading pca from: exp/enroll_train_wp
Top 1: 59.71 (speaker accuracy) 2.36 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 13.88
EER: 13.70
Cllr (min/act): 0.466674 0.848863
linkability: 0.638811./run.sh --stage 1 --frontend-train "--pca --pca_n_dim 70" --retrained-anon-xtractor true --oracle-f true
./run.sh --stage 1 --frontend-train "--pca --pca_n_dim 70" --retrained-anon-xtractor true --oracle-m trueresults (merged):
== Data used to train rotation ==
- xvect_libri_test_trials_f
- xvect_libri_test_trials_f_anon
Filtering by gender f
x_vector_l samples: 734
x_vector_u samples: 734
x_vector_l samples after filtering: 734
x_vector_u samples after filtering: 734
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (734, 70) with a total explained variance ratio on clear data: 0.9897747
Output shape after PCA: (734, 70) total explained variance ratio on target(anonymized) data: 0.97263634
Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 98.77 (speaker accuracy) 14.31 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Loading pca from: exp/enroll_train_wp
Top 1: 98.77 (speaker accuracy) 14.31 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 12.10
EER: 12.11
Cllr (min/act): 0.444288 0.863162
linkability: 0.710707
== Data used to train rotation ==
- xvect_libri_test_trials_m
- xvect_libri_test_trials_m_anon
Filtering by gender m
x_vector_l samples: 762
x_vector_u samples: 762
x_vector_l samples after filtering: 762
x_vector_u samples after filtering: 762
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (762, 70) with a total explained variance ratio on clear data: 0.98975855
Output shape after PCA: (762, 70) total explained variance ratio on target(anonymized) data: 0.98012084
Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 98.03 (speaker accuracy) 11.55 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Loading pca from: exp/enroll_train_wp
Top 1: 98.03 (speaker accuracy) 11.55 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 8.73
EER: 8.47
Cllr (min/act): 0.303992 0.840626
linkability: 0.791232./run.sh --stage 1 --frontend-train "--pca --pca_n_dim 70" --wp true --retrained-anon-xtractor true --oracle-f true
./run.sh --stage 1 --frontend-train "--pca --pca_n_dim 70" --wp true --retrained-anon-xtractor true --oracle-m trueresults (merged):
== Data used to train rotation ==
- xvect_libri_test_trials_f
- xvect_libri_test_trials_f_anon
Filtering by gender f
x_vector_l samples: 734
x_vector_u samples: 734
x_vector_l samples after filtering: 734
x_vector_u samples after filtering: 734
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (734, 70) with a total explained variance ratio on clear data: 0.98977935
Output shape after PCA: (734, 70) total explained variance ratio on target(anonymized) data: 0.97263455
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 99.32 (speaker accuracy) 8.45 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_f anonymized => procrustes => trials_f - original**
Loading pca from: exp/enroll_train_wp
Top 1: 99.32 (speaker accuracy) 8.45 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_f anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 13.09
EER: 13.00
Cllr (min/act): 0.463425 0.866338
linkability: 0.694130
== Data used to train rotation ==
- xvect_libri_test_trials_m
- xvect_libri_test_trials_m_anon
Filtering by gender m
x_vector_l samples: 762
x_vector_u samples: 762
x_vector_l samples after filtering: 762
x_vector_u samples after filtering: 762
== Training rotation ==
Computing PCA, 70 dimensions
Output shape after PCA: (762, 70) with a total explained variance ratio on clear data: 0.9897554
Output shape after PCA: (762, 70) total explained variance ratio on target(anonymized) data: 0.9801236
Wasserstein Procrustes rotation estimation
Compute done, rotation shape : (70, 70)
Top 1: 98.82 (speaker accuracy) 7.35 (segment accuracy)
Done
== TEST rotation irreversibility ==
**Accuracy trials_m anonymized => procrustes => trials_m - original**
Loading pca from: exp/enroll_train_wp
Top 1: 98.82 (speaker accuracy) 7.35 (segment accuracy)
== TEST likability between Anonymized and Orignal speech ==
Anonymized x-vector -> (extracted by a x-vector trained on anonymized speech)
Original x-vector -> (extracted by a x-vector trained on anonymized speech)
Loading pca from: exp/enroll_train_wp
**ASV: test_trials_m anonymized => procrustes <=> test_enrolls - original**
Loading pca from: exp/enroll_train_wp
ROC_EER: 10.49
EER: 10.11
Cllr (min/act): 0.342906 0.849621
linkability: 0.746411Put your cursor on the shell command and type @q (@b for the second line if any).
The coorsponding result block will be updated.
The q and b macro definitions (be a ; : remapper!):
let @q = '^"ay$/wiki
jV/``
kdk;read ! ��a --verbose "--noverbose" | sed "s,\x1B\[[0-9;]*[a-zA-Z],,g"
'
let @b = '^"ay$/wiki
j/``
ko�o�;read ! ��a --verbose "--noverbose" | sed "s,\x1B\[[0-9;]*[a-zA-Z],,g"
'