# Voice Deepfake Detection Using the Self-Supervised Pre-Training Model HuBERT
## 4 Experiment
### Experimental Results and Analysis
#### 4.2.2 Ablation Experiments  
To verify the improvement of detection performance, this study adopted the ablation experiments on the ASVspoof 2021 LA dataset, using the RawNet2 model as the base network (row 4 in Table 3). Data augmentation, the \(\alpha\)-FMS module, and a self-supervised speech pre-training and fine-tuning module were gradually added, and they were compared with the sinc filter of RawNet2. The experimental results are shown in Table 6 (\"\(-\)\" means the method is not included, \"\(\surd\)\" means the method is included). Figure 6 plots the performance results of the ablation experiments. The closer the data are to the origin of the coordinates, the better the detection effect and generalizability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Method**} & \multicolumn{3}{c}{**Metric**} \\ \cline{2-7}
**Abbreviation** & \multicolumn{3}{c}{**Front End**} & \multicolumn{3}{c}{**Data Augmentation**} & \multicolumn{1}{c}{\(\alpha\)**-FMS**} & \multicolumn{1}{c}{**EER (\%)**} & \multicolumn{1}{c}{**Min t-DCF**} \\ \cline{2-7}  & **SSL Pre-Trained and Fine-Tuned** & **Sinc Filter** & & & & \\ \hline SSL & \(\surd\) & – & – & – & 5.49 & 0.3687 \\ +DA & \(\surd\) & – & \(\surd\) & – & 4.89 & 0.3357 \\ +\(\alpha\)-FMS & \(\surd\) & – & – & \(\surd\) & 4.51 & 0.3268 \\ +DA+\(\alpha\)-FMS & \(\surd\) & – & \(\surd\) & \(\surd\) & 2.89 & 0.2182 \\ Sinc & – & \(\surd\) & – & – & 10.17 & 0.5103 \\ +DA & – & \(\surd\) & \(\surd\) & – & 9.19 & 0.4761 \\ +\(\alpha\)-FMS & – & \(\surd\) & – & \(\surd\) & 8.25 & 0.4573 \\ +DA+\(\alpha\)-FMS & – & \(\surd\) & \(\surd\) & \(\surd\) & 5.52 & 0.3964 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments of HuRawNet2_modified.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & **EER (\%)** & **Min t-DCF** \\ \hline ASVspoof 2021 LA (WenetSpeech) & 3.01 & 0.2278 \\ ASVspoof 2021 LA (LibriSpeech) & 2.89 & 0.2182 \\ ASVspoof 2019 LA (WenetSpeech) & 2.12 & 0.1442 \\ ASVspoof 2019 LA (LibriSpeech) & 1.96 & 0.1393 \\ FMFCC-A (WenetSpeech) & 3.25 & 0.3121 \\ FMFCC-A (LibriSpeech) & 3.37 & 0.3378 \\ FAD (WenetSpeech) & 8.31 & 0.1730 \\ FAD (LibriSpeech) & 9.75 & 0.2292 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of HuRawNet2_modified model on different datasets using different language pre-trained models.  
In terms of EER for comparison, it can be seen from rows 2 and 6 of Table 6 that adding the data augmentation module resulted in a 10.6\(\sim\)12.3% gain to the model; adding only \(\alpha\)-FMS in rows 3 and 7, the model performance was improved by approximately 17.8-18.9%; adding both data augmentation and \(\alpha\)-FMS, the EER was reduced by approximately 45.7-47.3% from rows 4 and 8, indicating that both data augmentation and \(\alpha\)-FMS contribute to the model performance improvement, with \(\alpha\)-FMS adding more to the model. From Figure 6, it can be seen that the self-supervised pre-training and fine-tuning front end was closer to the origin than the sinc filter's front end. It can be concluded that the EER and min t-DCF of the method based on self-supervised pre-training and fine-tuning proposed in this paper keep decreasing. The results were generally better than those of the front end of the sinc filter, which further verifies that the method proposed in this paper can fully extract deepfake speech features, and improve the detection effect and generalizability compared with mainstream detection algorithms.