# Voice Deepfake Detection Using the Self-Supervised Pre-Training Model HuBERT
## 4 Experiment
### Experimental Results and Analysis
#### 4.2.1 Comparison Experiments  
The EER and min t-DCF of the baseline model and HuRawNet2_modified method for the evaluation set of the ASVspoof 2021 challenge on the Asvspoof 2021 dataset are shown in Table 3. \"WenetSpeech\" indicates pre-training on WenetSpeech Chinese dataset, and \"LibriSpeech\" indicates pre-training on LibriSpeech English dataset. The analysis of Table 3 shows that the detection performance of the proposed method was significantly improved compared with the other four baseline models in the ASVspoof 2021 competition. Compared with Baseline RawNet2, the EER and min t-DCF indicators were reduced by 69.5% and 48.7%, respectively. This proves that the method of using \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **EER (\%)** & **Min t-DCF** \\ \hline CQCC-GMM [28] & 15.62 & 0.4974 \\ LFCC-GMM [29] & 19.30 & 0.5758 \\ LFCC-LCNN [30] & 9.26 & 0.3445 \\ Raw audio-RawNet2 [3] & 9.50 & 0.4257 \\
**HuRawNet2\_modified (LibriSpeech)** & 2.89 & 0.2182 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance for the ASVspoof 2021 evaluation partition in terms of EER (%) and min t-DCF. the self-supervised pre-training model to extract general features and then fine-tuning is more suitable for audio deepfake detection tasks. It can be seen that the self-supervised pre-training model was of practical value. The \(EER\) and \(Log-loss\) of the baseline models and HuRawNet2_modified method for the development set of the FMFCC-A dataset of the second Fake Media Forensic Challenge of CSIG are shown in Table 4.  
To verify the performance of this model on Chinese deepfake speech, the FMFCC-A and FAD datasets have been introduced in this paper. Analysis of the results in Table 4 shows that the EER of the model proposed in this paper was reduced by 55.3% and 60.7%, and the \(Log-loss\) was reduced by 42.2% and 61.7% for the FMFCC-A dataset and the FAD dataset, respectively, compared to the two baseline systems. This indicated that the performance of the model had been improved and the detection performance of Chinese faked speech was better. The LCNN-LSTM model, among the four baseline models of the ASVspoof2021 challenge, achieved the best result on the FAD dataset (\(EER=13.91\)), and the other specific results are not displayed in this paper. As compared to the LCNN-LSTM model, the EER of HuRawNet2_modified was reduced by approximately 29.9% and 40.25% for the FMFCC-A dataset and the FAD dataset, respectively. Therefore, we can conclude that the performance was improved compared to the baseline model on different datasets, indicating that HuRawNet2_modified model has a better generalizability and a certain advantage in terms of detection performance. In order to verify the above conclusions, this study used four datasets for testing, and the results are shown in Table 5 and Figure 5. The results of pre-training using different language datasets on the three datasets showed that the EER and min t-DCF were slightly reduced  
A self-supervised speech model pre-trained on different language datasets, using the same network model, was used to extract features, and fine-tune and test different language datasets with different detection performances. As is shown in rows 1 and 2 of Table 5, fine-tuning and detection on the ASVspoof 2021 dataset provided a gain of approximately 4.1% to the model, when using the pre-trained model trained on the LibriSpeech English dataset over the WenetSpeech Chinese dataset. As is shown in rows 3 and 4 of Table 5, fine-tuning and testing on the ASVspoof 2019 dataset provided a gain of approximately 7.5% to the model when using the pre-trained model trained on the LibriSpeech English dataset than when using the WenetSpeech Chinese dataset. As is shown in rows 5 and 6 of Table 5, fine-tuning and detection on the FMFCC-A dataset provided a gain of approximately 3.6% to the model when using the pre-trained model trained on the WenetSpeech Chinese dataset over the LibriSpeech English dataset.