SASVC

Spoofing-Aware Speaker Verification More information can be found in the webpage. This project provide three ways to realize Sooofing-aware Speaker Verification system. Score Fusion, embedding fusion and muti-task learning.

Prepare

1. Prepare embedding file

You can refer this offical project. Download the Embedding file. Put the embeddings floder to SF and EF folder.

2. Tools

This project need install this tools first 1. kaldi 2. ASV-subtools 3. speechbrain

Score Fusion

$$S_{sasv} = S_{cm} * sigmoid（S_{asv}）$$

ASV: ECAPA-TDNN + PLDA + LIP Adaptation SV-EER 1.47%

It takes PLDA backend as classifier, in this way. Unsupervised domain adaptation and supervised domain adaptation were applied in score fusion method to improve the speaker verification performance.

CM: wav2vev-ASSIST(Pretrain + Finetune CM-EER 0.20%)

score produced by Wav2Vec-AASIST EER was 0.20%. The sasv score is the multiplication of asv score and cm score processed by sigmoid function.

$ cd SF && ./run.sh

Embedding Fusion

Using Conv1D layers and SEModule to train a SASV model using embeddings from pre-trained asv system and countermeature system provided by SASV baseline system.

Training it:

cd EF && python main.py --config ./configs/sasv.conf

Multi-task

In this method, i used a pretrained asv system and Attentive statistic pooling layers and fusion backend to build a SASV model. In this way, speechbrain toolkit is needed.

Train:

cd EF && python train.py yaml/sasv.yaml

Test:

python eval.py yaml/sasv.yaml

Results(Eval set):

Model	SASV(EER%)	SV(EER%)	SPF(EER%)
Score Fusion	1.06	1.53	0.64
Embedding Fusion	0.96	1.24	0.68
Multi-task	3.24	3.99	1.64