# Voice Deepfake Detection Using the Self-Supervised Pre-Training Model HuBERT
## 2 Related Work  
### Detection Methods Based on Traditional Features and Related Events  
Early audio deepfake detection mainly relied on hidden Markov chains and Gaussian mixture models, and later evolved into front-end and back-end models. The typical audio deepfake detection system is a framework composed of a front end and back end. The front ends extract acoustic features from speech, and the back end converts features into scores. Traditional front-end feature extractors use digital signal processing algorithms to extract spectrum, phase, or other acoustic features. Among them, the most widely used include mel-frequency cepstral coefficients (MFCC), linear frequency cepstral coefficients (LFCC), and constant-Q transform cepstral features (CQCC) [1]. However, the detection method based on traditional features will result in the loss of some information. Moreover, it is usually only effective for detecting specific types of voice deepfakes, and generalization and robustness need to be improved.  
The distinguishing features of the front end of the traditional detection system adopt the hand-crafted features designed by experts, and the back end directly uses Gaussian mixture models (GMM) or support vector machine (SVM) for classification and judgment. In recent years, deep-learning-based systems have gradually become mainstream. The front ends extract the speech features of the input neural network, and the back end learns the high-level representation of the features through the neural network and then performs a classification judgment to identify the authenticity of the audio [2]. With the development of deep learning, it is increasingly common to use a deep neural network (DNN) to process the original waveform directly in many tasks. [PERSON] et al. [3] applied the improved RawNet2 network to synthetic speech detection, used a set of sinc filters to operate the original waveform through time-domain convolution directly, and then learned deep-level discriminative information through the residual module and gate recurrent unit(GRU). Based on this network, the RawGAT-ST model was proposed [4], and the spectro-temporal graph attention network was used to model the relationship across different sub-bands and temporal segments. Based on ResNet's skip layer connection and Inception's parallel convolution structure, [PERSON] et al. [2] designed two lightweight end-to-end time-domain synthetic speech detection networks (TSSDNet).  
In order to promote the research of audio deepfake detection technology, the Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof), jointly launched by the University of Edinburgh and other research institutions, has been held four times since 2015. This is the main event regarding audio deepfake detection [5]. The ASVspoof 2019 challenge [6] was the first challenge to consider three kinds of spoofing attacks simultaneously. The minimum tandem detection cost function (min t-DCF) was introduced to represent the performance of the whole system. The three sub-tasks of the ASVspoof 2021 challenge [7] focused on logical access(LA), physical access (PA), and speech deepfake (DF) tasks. In order to promote the development of audio deepfake detection in Chinese scenes, domestic scholars have also launched Chinese audio deepfake events, such as the second Fake Media Forensic Challenge of CSIG [8] and the third Chinese AI competition.