# Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models
## 6 Conclusions and Future Works  
Spoofing detection approaches is crucial to protect the user data against voice spoofing attacks while using ASV. These spoofing detection approaches amount to a classification problem where audio utterances are categorized into genuine or spoofed classes. However, this task remains challenging due to the high variance of the utterances. This factor affects the model's generalization for unseen utterances.  
In this paper, we devised a new replay countermeasure to address the high variance of these utterances. This countermeasure was performed by dividing the challenging classification problem into a set of local subproblems by mining the hidden structure of the data. Then, ensemble learning was used to combine these submodels. Various features, clustering techniques, classifiers, and their combinations were investigated. The experiments showed that CA clustering can automatically learn the number of homogeneous partitions of the data. Moreover, the experimental results showed that CQCC audio features along with the SVM classifier and SCAD clustering technique are the most suitable techniques to build the proposed approach. As a result, the latter method outperformed state-of-the-art approaches. Furthermore, when combining the results of the three classifiers (SVM, GMM, and XGBoost), the proposed approach achieved even better results.  
In future work, other audio features, classifiers, clustering techniques, and ensemble learning strategies could be investigated. Moreover, the performance of the proposed approaches on other types of voice spoofing could be explored.  
Conceptualization, S.M.A., O.B. and M.M.B.I.; methodology, S.M.A., O.B. and M.M.B.I.; software, S.M.A.; validation, S.M.A., O.B. and M.M.B.I.; formal analysis, S.M.A., O.B.; investigation, S.M.A. and O.B.; resources, S.M.A.; data curation, S.M.A.; writing--original draft preparation, S.M.A.; writing--review and editing, O.B. and M.M.B.I.; visualization, S.M.A.; supervision, O.B. and M.M.B.I.; project administration, S.M.A., O.B. and M.M.B.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**ASVspoof 2019**} & \multicolumn{3}{c}{**ASVspoof 2017 v2**} \\ \cline{2-7}  & **No. of Clusters** & \begin{tabular}{c} **Training** \\ **ERR (\%)** \\ \end{tabular} & **Testing** & **No. of Clusters** & \begin{tabular}{c} **Training** \\ **ERR (\%)** \\ \end{tabular} & **\begin{tabular}{c} **Testing** \\ **ERR (\%)** \\ \end{tabular} \\ \hline Baseline System 1 [4] & - & 9.87 & 11.04 & - & 10.35 & 24.77 \\ \hline Baseline System 2 [68] & \begin{tabular}{c} 15 for genuine and 15 \\ for spoofing \\ \end{tabular} & 2.30 & 3.31 & \
\begin{tabular}{c} 2 for genuine and 2 \\ for spoofing \\ \end{tabular} & 0.13 & 1.07 \\ \hline Baseline system 3 [46] & - & 0.129 & **0.59** & - & - & - \\ \hline Local-SVM-based approach & 2 & 0.075 & 0.149 & 2 & 0.067 & 0.154 \\ \hline Local-GMM-based approach & 2 & 0.189 & 0.59 & 4 & 0.151 & 0.302 \\ \hline Local-XGBoost-based approach & 3 & 0.74 & 1.36 & 3 & 0.435 & 1.07 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Local-Ensemble Learning-based \\ approach \\ \end{tabular} } & 2, 2, 3 & 0.06 & **0.119** & 2, 4, 3 & 0.0385 & **0.097** \\ \hline \hline \end{tabular} Bold number indicates the lowest EER value.  
\end{table}
Table 10: Performance comparison with state-of-the-art approaches.  
Data Availability Statement: The ASVspoof 2017 version 2.0 and ASVspoof 2019 benchmark datasets can be found at [[https://www.asvspoof.org/database](https://www.asvspoof.org/database)]([https://www.asvspoof.org/database](https://www.asvspoof.org/database)) (accessed on 1 September 2021).  
This work was supported by the Research Center of the College of Computer and Information Sciences at King Saud University. The authors are grateful for this support.  
The authors declare no conflict of interest.