# Audio Anti-Spoofing Based on Audio Feature Fusion
## 1 Introduction  
In recent years, many researchers have proposed end-to-end speech synthesis systems based on deep learning, such as Tacotron2 [1], Deep Voice 3 [2], Transformer TTS [3], and FastSpeech 2 [4]. Synthesized speech has dramatically improved in human-likeness, realism, and naturalness. Speech synthesis software for different languages (such as Chinese, English, Vietnamese, etc.) has been made available to the public, with reduced barriers to entry and difficulty. The highly authentic synthetic speech segments mentioned will pose significant challenges to human auditory perception. Moreover, if these synthesized speech segments are maliciously exploited in authentication systems such as ASV, they can pose a grave security threat. Training a traditional ASV system that is robust to attacks with synthetic speech requires a dataset that includes various types of synthetic speech generated by different algorithms. However, it is not practical to construct a training dataset that contains all possible attack scenarios, as attackers will always use unknown attack methods to target ASV systems. Therefore, when designing ASV systems, reliability in the face of various and unpredictable attacks [5] needs to be considered, and the generalization ability of ASV systems needs to be improved. The ASVspoof Challenge has collected a large amount of speech segment data suitable for training ASV systems and constructed a public dataset [5, 6, 7, 8] to improve the detection capability of ASV systems against unknown spoofing attacks. The task of the ASVspoof2019 Challenge includes two scenarios: logical access (LA) and physical access (PA). The LA scenario consists of two attacks against ASV systems: speech synthesis and conversion. The PA scenario explicitly refers to the attack method of recording and replaying. The ASVspoof 2021 Challenge adds a deep-fake (DF) partition aimed at addressing the scenario of speech synthesis using deep-fake technology. This research mainly focuses on the LA and DF scenarios. In recent years, more and more researchers have attempted to improve the detection capability of ASV systems by building deep learning models. Many studies [9; 10; 11] have proposed effective network architectures, such as using raw waveforms as input to obtain better representations. However, the robustness of the model is still limited by the insufficiency of the training data. To address this issue, some recent studies [12; 13] have focused on using a pre-trained model at the front end of the existing model and using transfer learning to obtain more generalized feature representations. However, most recent research has focused solely on the ASVspoof 2019 dataset, with very little attention given to ASVspoof 2021, particularly the DF partition. ASVspoof 2021 DF is a new task within ASVspoof, which extends the focus of the ASVspoof Challenge beyond ASV scenarios to detecting spoofed speech in non-ASV contexts. The DF task reflects scenarios in which attackers access victims' speech data, such as data published by victims on social media. Attackers may use public data and deep-fake technologies to generate synthetic speech with the voice of a victim and use such fake speech to engage in fraudulent activities, such as spreading false information, blackmailing, and causing social unrest and panic. They may also generate synthetic speeches of influential figures in social media or politics to spread fake news and some discord or create artificial voices of ordinary individuals to deceive their family members of fraud. Additionally, attackers may generate harmful speech data to defame their victims. Therefore, developing better techniques for detecting synthetic speech generated using deep-fake technologies is an urgent problem that needs to be addressed. The main contribution of this paper is designing a model with a lower equal error rate (EER) on both the ASVspoof 2021 LA and DF partitions. We use a pre-trained feature extraction model and a designed audio feature fusion mechanism for back-end classification.