Speaker verification using Gaussian Mixture Model (GMM).
- Run in google colab
accuracy score for DR3_FPKT0: 100.0%
eer score for DR3_FPKT0: 11.377245508993903% with threshold -7.957904145487239
precision recall f1-score support
0 1.00 1.00 1.00 167
1 1.00 1.00 1.00 10
accuracy 1.00 177
macro avg 1.00 1.00 1.00 177
weighted avg 1.00 1.00 1.00 177
accuracy score for DR3_MJJG0: 98.87005649717514%
eer score for DR3_MJJG0: 13.7724550898456% with threshold -7.269569053999492
precision recall f1-score support
0 0.99 0.99 0.99 167
1 0.90 0.90 0.90 10
accuracy 0.99 177
macro avg 0.95 0.95 0.95 177
weighted avg 0.99 0.99 0.99 177
accuracy score for DR3_FCMH0: 99.43502824858757%
eer score for DR3_FCMH0: 1.1976047904198035% with threshold -6.315210336766802
precision recall f1-score support
0 0.99 1.00 1.00 167
1 1.00 0.90 0.95 10
accuracy 0.99 177
macro avg 1.00 0.95 0.97 177
weighted avg 0.99 0.99 0.99 177
accuracy score for DR3_MWJG0: 99.43502824858757%
eer score for DR3_MWJG0: 10.000000000018641% with threshold -7.284514182552126
precision recall f1-score support
0 0.99 1.00 1.00 167
1 1.00 0.90 0.95 10
accuracy 0.99 177
macro avg 1.00 0.95 0.97 177
weighted avg 0.99 0.99 0.99 177
accuracy score for DR3_MTAA0: 99.43502824858757%
eer score for DR3_MTAA0: 4.790419161838868% with threshold -6.471338153605574
precision recall f1-score support
0 0.99 1.00 1.00 167
1 1.00 0.90 0.95 10
accuracy 0.99 177
macro avg 1.00 0.95 0.97 177
weighted avg 0.99 0.99 0.99 177
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Final average test accuracy: 2.959375840731773%
Final average test EER: 0.244867408042362%
Final accuracy score for all speakers: 99.43502824858757%
Final eer score for all speakers: 9.131652661064422%
precision recall f1-score support
0 1.00 1.00 1.00 835
1 0.98 0.92 0.95 50
accuracy 0.99 885
macro avg 0.99 0.96 0.97 885
weighted avg 0.99 0.99 0.99 885
...
Tested with:
python3.6python3.7python3.8
TO-DO:
- gmm-ubm
- svm
- inference script
React
Typescript
Django
Laravel
Facebook
Microsoft
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Alibaba
D3
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