# Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models
## 5 Experiments
### Experiment 1: Number of Clusters and Audio Feature Investigation  
In this experiment, the FCM [14] clustering approach was employed to mine the hidden structure of the data. This approach partitions the whole ASVspoof 2017 benchmark dataset into homogeneous local subgroups. Each subgroup contained genuine and replay spoofed utterances, the latter of which constituted a local classification subproblem. Two classifiers, SVM [17] with linear kernel and GMM [6] with two mixture components, were utilized to solve these subproblems. Furthermore, to explore the structure of the data, different numbers of clusters were considered. These numbers were tuned between 2 and 15. Moreover, the data were clustered using CQCC, MFCC, and LFCC features. Each feature was considered independently and concatenated together. Figures 5 and 6 depict the EER obtained with MFCC, QCC, LFCC, and concatenation together with respect to the cluster number when considering SVM and GMM classifiers, respectively. The results indicate that performance varied with respect to the number of clusters, the type of audio features, and the classifier.  
For SVM-based systems, CQCC features generally performed better than the other considered features, especially when the cluster number was less than 9. However, the best performance was achieved with two clusters. Alternatively, for GMM-based approaches, the best performance was attained with four clusters. Nonetheless, CQCC remained the best performing feature type. Table 5 reports the best performance achieved by each combination of feature/classifier for the optimal number of clusters. The system that used CQCC features with two clusters had the smallest EER (1.61%) and thus outperformed the other combinations. The second best was the system employing CQCC features and GMM, with an ERR of 4.23%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Subset**} & \multirow{2}{*}{**No. of Speakers**} & \multicolumn{2}{c}{**No. of Utterances**} \\ \cline{3-4}  & & **Genuine** & **Spoofed** \\ \hline Training & 20 & 5400 & 48,600 \\ \hline Development & 20 & 5400 & 24,300 \\ \hline Evaluation & - & 137,457 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: ASVspoof 2019 replay spoofing dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Subset**} & \multirow{2}{*}{**No. of Speakers**} & \multicolumn{2}{c}{**No. of Utterances**} \\ \cline{3-4}  & & **Genuine** & **Spoofed** \\ \hline Training & 10 & 1507 & 1507 \\ \hline Development & 8 & 760 & 950 \\ \hline Evaluation & 24 & 1298 & 12,008 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ASVspoof 2017 V2.0 dataset.  
Figure 5: EER obtained with MFCC, QCC, LFCC, and concatenation together with respect to the number of clusters when using SVM on the ASVspoof 2017 version 2.0 benchmark dataset.  
Figure 6: EER obtained by MFCC, QCC, LFCC, and concatenation together with respect to the number of clusters when using GMM on the ASVspoof 2017 version 2.0 benchmark dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **No of Clusters** & **EER\%** \\ \hline SVM with MFCC & 2 & 9.16 \\ \hline SVM with CQCC & 2 & **1.61** \\ \hline SVM with LFCC & 7 & 8.62 \\ \hline SVM with ALL & 3 & 3.85 \\ \hline GMM with MFCC & 13 & 20.18 \\ \hline GMM with CQCC & 4 & 4.23 \\ \hline GMM with LFCC & 8 & 14.63 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Best performance achieved by each combination of feature/classifier for the optimal number of partitions on the ASVspoof 2017 benchmark dataset.