## 4 Results
### Validation Performances on Noisy Data  
Table 4 reports the performances for all the possible combinations of classifiers and feature families. For the scope of the present study, consistency and automatization were crucial as the focus was not on building a high-performance speaker verification system, but on assessing its behavior, once trained, when new data were noisy or reverberated. ### Validation Performances on Noisy Data  
Table 4 reports the performances for all the possible combinations of classifiers and feature families. \begin{table}
\begin{tabular}{c c c c c c} \hline
**BA** & **Sens** & **Spec** & **Class.** & **Aug** & **Cat.** \\ \hline
0.750 & 0.981 & 0.512 & SVM & raw & Merged \\
0.748 & 0.977 & 0.519 & SVM & raw & MFCC \\
0.651 & 0.937 & 0.365 & SVM & raw & RASTA \\
0.468 & 0.511 & 0.425 & SVM & noise\_25 & Merged \\
0.490 & 0.381 & 0.599 & SVM & noise\_25 & MFCC \\
0.407 & 0.437 & 0.377 & SVM & noise\_25 & RASTA \\
0.451 & 0.478 & 0.424 & SVM & noise\_20 & Merged \\
0.488 & 0.363 & 0.612 & SVM & noise\_20 & MFCC \\
0.425 & 0.470 & 0.379 & SVM & noise\_20 & RASTA \\
0.450 & 0.474 & 0.425 & SVM & noise\_15 & Merged \\
0.476 & 0.322 & 0.631 & SVM & noise\_15 & MFCC \\
0.412 & 0.444 & 0.380 & SVM & noise\_15 & RASTA \\
0.469 & 0.511 & 0.426 & SVM & noise\_10 & Merged \\
0.477 & 0.296 & 0.657 & SVM & noise\_10 & MFCC \\
0.415 & 0.452 & 0.379 & SVM & noise\_10 & RASTA \\
0.452 & 0.478 & 0.426 & SVM & noise\_5 & Merged \\
0.490 & 0.278 & 0.703 & SVM & noise\_5 & MFCC \\
0.429 & 0.485 & 0.372 & SVM & noise\_5 & RASTA \\
0.456 & 0.444 & 0.468 & SVM & rev\_—40 & Merged \\
0.473 & 0.422 & 0.524 & SVM & rev\_—40 & MFCC \\
0.399 & 0.467 & 0.332 & SVM & rev\_—40 & RASTA \\ \hline \end{tabular}
\end{table} 
Table 4: Performances of the three classifiers on all versions of the dataset. BA = balanced accuracy; Sens = sensitivity; Spec = specificity; Class. = classifier; Aug = type of audio data (raw, noise with a given SNR, or reverb with a given wet signal in dB); Cat = category or feature family; SVM = support vector machine; RF = Random Forest; AB = Adaboost. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{2}{c}{**MFCC**} & \multicolumn{2}{c}{**RASTA**} & \multicolumn{2}{c}{**Merged**} \\ \hline  & **BA** & **Sens** & **Spec** & **BA** & **Sens** & **Spec** & **BA** & **Sens** & **Spec** \\ \hline SVM & 74.8 & 97.7 & 51.9 & 65.1 & 93.7 & 36.5 & 75 & 98.1 & 51.2 \\  & (99–643) & (100–92.5) & (78–30.1) & (70–52.7) & (100–77.8) & (44.8–27.8) & (99–56.0) & (100–92.6) & (68.6–21.2) \\ \hline RF & 68.3 & 97.9 & 38.8 & 65.8 & 91.2 & 40.3 & 70 & 97.6 & 42 \\  & (06.8–51.1) & (100–90.1) & (93–75.7) & (86–153.4) & (100–77.8) & (72.3–19.3) & (98.6–52.2) & (100–88.9) & (97–18.7) \\ \hline AB & 69.2 & 97.1 & 41.4 & 67 & 89.4 & 44.5 & 71 & 97.5 & 44.4 \\  & (97.7–52.1) & (100–88.9) & (95.5–6.7) & (86.2–56.6) & (100–74.1) & (76–20.1) & (98.5–52.1) & (100–88.9) & (96.9–10.1) \\ \hline \end{tabular}
\end{table} 
Table 3: The performances of the three classifiers on the raw dataset. All metrics are expressed in percentages and also report maximum and minimum values. Abbreviations: BA = balanced accuracy; Sens = sensitivity; Spec = specificity; SVM = support vector machine; RF = Random Forest; AB = Adaboost.