This code is forked from entn-at/tf-kaldi-speaker. It is a speaker verification system based on Kaldi and TensorFlow. More detail please refer: entn-at/tf-kaldi-speaker.
This version has two features compared with the original branch:
This is a famouse Resnet topology, the blocks are: [3/32, 3/32], [3/64, 3/64], [3/128, 3/128], [3/256, 3/256], and the number of blocks is: [3, 4, 6, 3]
The code is /model/resnet.py.
A SITW recipe is added in egs/sitw, which is largely based on SITW offical x-vector recipe.
There are 8 exprimental settings in the SITW recipe, with different network topologies, pooling methods and loss functions. See ./egs/sitw/v1/nnet_conf. Note that the training and test data are the same as SITW offical recipe.
Some of the experimental results are shown below:
| Topoloy | Pooling | Loss func | EER(%) |
|---|---|---|---|
| TDNN | Statistic Pooling | Softmax | 2.43 |
| TDNN | Attention Pooling | AAM-Softmax | 2.49 |
| TDNN | Statistic Pooling | Softmax | 2.41 |
| TDNN | Attention Pooling | AAM-Softmax | 2.57 |
| Resnet-34 | Statistic Pooling | Softmax | 2.41 |
| Resnet-34 | Attention Pooling | AAM-Softmax | 1.96 |
| Resnet-34 | Statistic Pooling | Softmax | 2.16 |
| Resnet-34 | Attention Pooling | AAM-Softmax | 2.30 |
Where "AAM" means additive angular margin.