# Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models
## 5 Experiments
### Experiment 4: Performance Comparison with Related Spoofing Detection Approaches  
According to the previous experiments' findings, the SCAD clustering algorithm with CQCC achieved the best performance with respect to the three considered classifiers. As such, in this experiment, we considered four versions of the proposed approach using CQCC for feature extraction and SCAD for mining the structure of the data. These approaches use SVM, GMM, XGBoost, and their combination. These methods are referred to as the local-SVM-based approach, local-GMM-based approach, local-XGBoost-based approach, and local-ensemble-learning-based approach. We also compared the performance of the proposed approaches to three state-of-the-art approaches. The first was the approach reported in [4], which consisted of extracting the CQCC feature and conveying it to a GMM-based classifier. The second approach, which is the most recent, was reported in [68], and it uses SCAD to cluster genuine utterances into G clusters, with the spoofed utterances placed into two S clusters. As such, this approach assigns the unknown instance as the class of the closest cluster (refer to Section 3.3). The third baseline approach, published in [46], was the best performing method for the ASVspoof replay 2019 dataset. This approach uses CQCCS and spectrogram features and conveys them to the SENet [47], Mean-Std ResNet [47], and Dilated ResNet [48] deep-learning models. Then, the greedy fusion scheme described in [49] was employed to explore the best system combination.  
For this purpose, we considered the two available replay datasets: ASVspoof 2017 v2 [72] and ASVspoof 2019 [73]. The same datasets with the same training and testing sets were employed for all considered approaches. To evaluate the generalization capabilities of the proposed approach, both the training and testing ERR were compared, as reported in Table 10, where the proposed approach based on ensemble learning outperformed all  
Figure 8: EER with respect to the number of clusters when using SCAD with GMM [6] on CQCC on the ASVspoof 2017 version 2.0 benchmark dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **No of Clusters** & **EER (\%)** \\ \hline SCAD + SVM & 2 & 0.154 \\ \hline SCAD + GMM & 4 & 0.302 \\ \hline SCAD + XGboost & 3 & 1.07 \\ \hline Majority vote on SVM, GMM, and XGboost & 2, 4, 3 & **0.097** \\ \hline \hline \end{tabular} Bold number indicates the lowest EER value.  
\end{table}
Table 9: EER for SCAD with different classifiers on the ASVspoof 2017 version 2.0 Benchmark Dataset.  
other considered systems with respect to the two datasets. Nonetheless, even without considering ensemble learning, the three other approaches achieved smaller EERs than the state-of-the-art ones, except for the local-GMM-based approach, which offered the same performance as baseline approach 3 on the ASVspoof replay 2019 dataset. This result was achieved by dividing the classification problem into sublocal problems to address the utterance high variance problem and was confirmed by the training and testing ERR results. The difference between the training and testing ERR was reduced. This result shows that the generalization problem was addressed.