# Audio Anti-Spoofing Based on Audio Feature Fusion
## 2 Methods
### Wav2 vec 2.0  
Traditional detection methods often use carefully designed handcrafted features, such as MFCC, LFCC, CQCC, and other mathematically-based audio feature maps. These audio features can be easily and quickly obtained and contain valuable information.  
In [16], the authors used LFCC and CQT as inputs to an end-to-end dual-branch network using a multitask learning method to detect different types of synthetic speech. Specifically, each branch added a forgery-type classifier as an additional task. Then, through adversarial training between the network and the forgery-type classifier, the learned features did not contain specific forgery information associated with a particular forgery type. Additionally, the convolutional block attention module (CBAM) was used to improve the representation ability of the learned features. Using handcrafted features effectively reduces the number of parameters in the model and speeds up the training process, but it may decrease the model accuracy.  
With the continuous development of deep learning, a SincNet-based audio feature extraction method has emerged, which is an interpretable neural network structure designed to process temporal speech signals and directly learn more meaningful filters. Generally, for processing temporal signals, the extraction capability of the first-layer network is crucial because the effectiveness of the low-dimensional features extracted in the first layer is a prerequisite for the high-dimensional feature information learned by the higher layers of the network to be meaningful. In RawGAT-ST [10], for instance, the model directly operates on the raw waveform and uses a Sinc convolutional filter for front-end feature learning. This approach of utilizing neural networks to extract front-end features effectively improves the accuracy of the model.  
However, both of the above feature extraction methods have certain limitations. Handcrafted features based on mathematical principles inevitably need to be aware of hidden information. Although neural network-based feature extraction can effectively extract hidden information, it is limited by the model parameters and training data size, leading to suboptimal performance.  
The emergence of transfer learning can effectively solve the above problems. Using pre-trained models can significantly reduce training time, and ideal results can be achieved with a small amount of data. Therefore, more and more researchers are using pre-trained models to extract features [12; 13; 17; 18] to improve model performance.  
Wav2 vec 2.0 [19] is a self-supervised learning framework proposed by Facebook AI, which introduces a transformer to replace RNN structures. The potent ability of the transformer makes wav2 vec 2.0 much better than previous models. Although wav2 vec 2.0 was initially used for automatic speech recognition (ASR), recent research, such as speech error detection [20; 21], speaker recognition [22; 23], emotion recognition [24], and deception detection [12; 25], has shown that wav2 vec 2.0 can also achieve good performance in other speech-related tasks. In this paper, we also used the wav2 vec 2.0 model to perform synthetic speech recognition using the pre-trained model.  
Since the pre-trained model only uses natural speech data (without synthetic speech data) for training, according to the research [12], fine-tuning with training data containing synthetic speech can potentially improve the model detection performance. To better adapt to downstream training tasks, we added a fully connected layer to the output of the wav2 vec 2.0 encoder.  
Figure 1: Overall framework diagram of our model. From the figure, it can be seen that the original waveform was input into wav2 vec 2.0 for feature extraction. Then, the features obtained through embedding were input into the feature fusion stage. The feature fusion stage is composed of four repeated remix and feature fusion blocks. The final output was obtained through a fully connected (FC) layer.