# Voice Deepfake Detection Using the Self-Supervised Pre-Training Model HuBERT
## 5 Conclusions and Discussion  
In this study, we designed an audio deepfake detection model based on a self-supervised pre-training model with improvements in two parts: front-end feature extraction and a back-end classification model. For front-end feature extraction, the model performed data augmentation using a self-supervised model to extract generic linguistic features, which were then fine-tuned in two separate datasets. On the back-end classification model, RawNet2 was improved by introducing \(\alpha\)-FMS to enhance the discrimination of the feature maps. With the continuous development of deepfake technology, detection technology will face a more severe test. In future studies, subsequent attempts will be made to integrate the system with the speech recognition system, improve generalizability, study the gains associated with different loss functions, and focus on the effectiveness of detecting various attacks.  
Subsequent research will concentrate on unlabeled detection techniques, encompassing methods such as self-supervised knowledge distillation, aiming to yield more pragmatically applicable solutions. We believe that this delineates one of the future trajectories for deep learning research. Confronted with the escalation of innovative and unfamiliar forgery methodologies, our objective remains steadfast in preserving the ability to detect audio forgeries, thus augmenting the model's generalization capability. Furthermore, the forthcoming ASVspoof 2023 challenge is on the horizon, prompting us to continually monitor and appreciate the advancements achieved in the realm of voice deepfake detection.  
Figure 6: The results of ablation experiments.  
**Author Contributions:** Conceptualization, L.L. and T.L.; methodology, X.M.; software, M.Y.; validation, L.L., D.W. and M.Y.; formal analysis, X.M.; investigation, D.W.; resources, X.M.; data curation, L.L.; writing--original draft preparation, L.L.; writing--review and editing, L.L.; visualization, T.L.; supervision, L.L.; project administration, T.L.; funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Double First-Class Innovation Research Project for People's Public Security University of China (No.2023 SYL07).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The ASVspoof 2021 dataset can be found at [[https://www.asvspoof.org/index2021.html](https://www.asvspoof.org/index2021.html)]([https://www.asvspoof.org/index2021.html](https://www.asvspoof.org/index2021.html)) (accessed on 28 May 2023). The ASVspoof 2019 dataset can be found at [[https://www.asvspoof.org/index2019.html](https://www.asvspoof.org/index2019.html)]([https://www.asvspoof.org/index2019.html](https://www.asvspoof.org/index2019.html)) (accessed on 28 May 2023). The FMFCC-A dataset can be found at [[https://github.com/Amforever/FMFCC-A](https://github.com/Amforever/FMFCC-A)]([https://github.com/Amforever/FMFCC-A](https://github.com/Amforever/FMFCC-A)) (accessed on 28 May 2023). The FAD dataset can be found at [[https://zenodo.org/record/6635521#.Ysj4](https://zenodo.org/record/6635521#.Ysj4) nZBw2x]([https://zenodo.org/record/6635521#.Ysj4](https://zenodo.org/record/6635521#.Ysj4) nZBw2x) (accessed on 28 May 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.