# Voice Deepfake Detection Using the Self-Supervised Pre-Training Model HuBERT
###### Abstract  
In recent years, voice deepfake technology has developed rapidly, but current detection methods have the problems of insufficient detection generalization and insufficient feature extraction for unknown attacks. This paper presents a forged speech detection method (HuRawNet2_modified) based on a self-supervised pre-trained model (HuBERT) to improve detection (and address the above problems). A combination of impulsive signal-dependent additive noise and additive white Gaussian noise was adopted for data boosting and augmentation, and the HuBERT model was fine-tuned on different language databases. On this basis, the size of the extracted feature maps was modified independently by the \(\alpha\)-feature map scaling (\(\alpha\)-FMS) method, with a modified end-to-end method using the RawNet2 model as the backbone structure. The results showed that the HuBERT model could extract features more comprehensively and accurately. The best evaluation indicators were an equal error rate (EER) of 2.89% and a minimum tandem detection cost function (min t-DCF) of 0.2182 on the database of the ASVspoof2021 LA challenge, which verified the effectiveness of the detection method proposed in this paper. Compared with the baseline systems in databases of the ASVspoof 2021 LA challenge and the FMFCC-A, the values of EER and min t-DCF decreased. The results also showed that the self-supervised pre-trained model with fine-tuning can extract acoustic features across languages. And the detection can be slightly improved when the languages of the pre-trained database, and the fine-tuned and tested database are the same.  
voice deepfake detection; self-supervised learning; pre-training; feature map scaling; anti-spoofing +
Footnote †: journal: Medical  
Current research shows that self-supervised speech models can extract robust acoustic features for unknown domains. In addition, the existing audio deepfake detection methods are easy to overfit on the training set and have poor robustness in terms of audio detection after recompression, noise addition, and other processing. Most of the existing audio deepfake detection methods rely on specific datasets or specific deepfake methods, with a single and homogeneous distribution of training data. Most are detected on English datasets, so their generalizability cannot be tested on Chinese datasets.  
To address these issues, this study proposed a self-supervised pre-trained model for brevity, namely HuRawNet2_modified. The main contributions are as follows:  
1. Regarding front-end feature extraction, self-supervised pre-training models trained on either English or Chinese datasets were used, and fine-tuning with English and Chinese datasets was undertaken to explore the impact of pre-training models using different language datasets on the results.
2. For the back-end model, an improved end-to-end RawNet2 model was used as the backbone structure, and \(\alpha\)-FMS was used to independently modify the size of the feature maps to improve the model detection and compare the performance with current state-of-the-art algorithms on Chinese and English datasets.
3. In terms of datasets, to address the problem of voice deepfake detection being trained chiefly and tested on English datasets, cross-library tests were conducted on different language datasets to verify the proposed method's detection performance and generalizability on Chinese and English datasets.