# Replay Speech Detection Based on Dual-Input Hierarchical Fusion Network
## 4 Results
### The System Performance of Different Acoustic Features  
This section evaluates the system performance of the dual-input hierarchical fusion network using different acoustic features of the ASVspoof 2021 PA test set, and compares it with that of the baseline system. Table 3 shows the min t-DCF and EER of the baseline system and our proposed system in the evaluation test. The experimental results show that using CQT as the model input feature achieved the best system performance. CQT is a classic time-frequency transform method with advantages such as higher frequency resolution, balanced time-frequency resolution, and lower computational complexity, and is widely used in fields such as music signal classification and speech anti-spoofing. Similarly, using LFCC as the model input feature also achieved desirable results. LFCC is the official baseline feature of ASVspoof and is an amplitude feature extraction method. Compared with traditional acoustic features, such as Mel-frequency cepstral coefficients, its filter bank is uniformly distributed over the entire frequency range; thus, itcan more comprehensively capture information in speech signals. In addition, using Spec as the model input feature achieved dissatisfactory results. This is because the Spec feature is the logarithmic amplitude spectrum and does not contain phase information. However, the synthetic speech in real-world environments may exist in the phase information of speech, so the system performance of the Spec feature is relatively inferior to the other two acoustic features.  
According to the results in Table 3, the HFN + CQT and HFN + LFCC models we proposed perform significantly better than all the baseline systems provided by the ASVspoof 2021 PA task. Among them, the HFN + LFCC model reduces the min t-DCF and EER relative to the baseline system by a maximum of 10.5% and 25.4%, respectively; the HFN + CQT model reduces the min t-DCF and EER relative to the baseline system  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input Methods** & **Acoustic Features** & **Models** & **Min t-DCF** & **EER (\%)** \\ \hline \multirow{2}{*}{Single-input} & CQT & \multirow{2}{*}{ResNet-50} & 0.9913 & 38.17 \\  & CQT (invert) & & 0.9848 & 41.12 \\ \hline \multirow{2}{*}{Overlay-input} & CQT + CQT (invert) & ResNet-50 & 0.9826 & 38.84 \\ \hline \multirow{2}{*}{Dual-input} & CQT + CQT (invert) & \multirow{2}{*}{HFN} & **0.7572** & **28.51** \\  & Double CQT & & **0.7741** & **29.33** \\ \cline{1-1}  & Double CQT (invert) & & **0.7709** & **29.63** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The EER (%) and min t-DCF of the proposed system corresponding to different input methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **System** & **Min t-DCF** & **EER (\%)** \\ \hline \multirow{4}{*}{Baseline} & CQCC + GMM & 0.9434 & 38.07 \\  & LFCC + GMM & 0.9724 & 39.54 \\  & LFCC + LCNN & 0.9958 & 44.77 \\  & RawNet2 & 0.9997 & 48.60 \\ \hline \multirow{4}{*}{Proposed} & HFN + Spec & 0.9453 & 45.15 \\  & HFN + LFCC & 0.8945 & 36.25 \\ \cline{1-1}  & HFN + CQT & **0.7572** & **28.51** \\ \cline{1-1}  & Fusion & **0.6708** & **24.46** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The EER (%) and min t-DCF of the proposed system corresponding to different acoustic features.  
by a maximum of 24.3% and 41.3%, respectively. The proposed fusion system is the weighted average of the scores of the three single systems: HFN + Spec, HFN + LFCC, and HFN + CQT. The fusion system achieves the best results in the final evaluation set, with an EER of 24.46% and a min t-DCF of 0.6708. Compared with the four baseline systems in the ASVspoof 2021 competition, in our fusion model, the min t-DCF values are decreased by 28.9%, 31.0%, 32.6%, and 32.9%, and the EER values are reduced by 35.7%, 38.1%, 45.4%, and 49.7%, respectively.