# Replay Speech Detection Based on Dual-Input Hierarchical Fusion Network  
[PERSON]  
1 School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102627, China 1, [PERSON]  
1 School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102627, China 1,*  
An analysis of the related models of ASVspoof 2017 and 2019 revealed that the most advanced spoofing detection systems are primarily founded upon the use of convolutional neural networks (CNNs). The differences among these systems mainly focus on front-end speech feature extraction [9; 10], back-end classification network architecture [11; 12; 13], and training strategies such as the loss function, learning rate scheduler, optimizer, etc. [14]. [PERSON] et al. [15] reported the best results in the ASVspoof 2017 competition by utilizing features such as spectrograms and identification vectors (I-Vectors) [16] as inputs, along with Light CNN (LCNN) and Recurrent Neural Network (RNN) classifiers. [PERSON] et al. [17] proposed an attention-enhanced DenseNet-BiLSTM network model, which employs segmented linear filter group features. Their findings show that the model based on segmented linear filter group features, along with attention-enhanced DenseNet-BiLSTM, can more effectively distinguish between replayed speech. The model achieved a relative EER reduction of 74.04% on the ASVspoof 2017 dataset compared to the baseline system, which was based on constant Q cepstral coefficients and Gaussian mixture models. [PERSON] et al. [18] found that using multiple classifiers can enable the detection of text-to-speech synthesis attacks, voice conversion attacks, and replayed speech threats. [PERSON] et al. [19] addressed the physical access problem of ASVspoof 2019 and proposed fusing Power Spectrum and Constant Q Transform (CQT) features, and trained two VGG networks. [PERSON] et al. [20] proposed a deep residual network (ResNet) with self-attention for speech anti-spoofing systems, which demonstrated powerful performance in identifying spoofing cues. [PERSON] et al. [21] used a variant of the convolutional neural network, Light CNN (LCNN), based on Max-Feature-Map activation (MFM), which performed excellently in speech anti-spoofing detection. [PERSON] et al. [22] investigated deep learning models for speech anti-spoofing detection and demonstrated that a combination of CNNs and Recurrent Neural Networks (RNNs) can improve the robustness of the system. [PERSON] et al. [23] presented a technique that incorporates the Res2 Net network for detecting replay and synthetic speech. The method utilized the Res2 Net network to partition the feature maps into multiple channel groups, and developed a residual-like connection between these groups, thereby expanding the receptive field and generating multiple feature scales. This approach effectively enhances the detection capacity of the anti-spoofing system against unobserved spoofing attacks. [PERSON] et al. [24] were the first to employ the RawNet2 network model in their anti-spoofing system. The method utilized RawNet2 to input raw audio, allowing it to detect some fake speech that traditional models may not. The system exhibited outstanding performance, and became the official baseline system for the ASVspoof 2021 challenge. [PERSON] et al. [25] introduced a multi-point input method based on convolutional neural networks for detecting replay speech attacks, which enhances the amount of information gained from a single model input. Additionally, the method was applied as a baseline for the ASVspoof 2019 PA track. [PERSON] et al. [26] demonstrated that using different front-end feature extractions, training the same back-end residual neural network, and model fusion can lead to better recognition results. These models showed strong capabilities in time domain and frequency domain modeling, and exhibited excellent performance on the ASVspoof 2019 dataset.