## 4 Results
### Validation Performances on Noisy Data
\begin{table}
\begin{tabular}{c c c c c c} \hline
**BA** & **Sens** & **Spec** & **Class.** & **Aug** & **Cat.** \\ \hline
0.456 & 0.444 & 0.468 & SVM & rev\({}_{-}\)\(-\)\(-\)33 & Merged \\
0.472 & 0.419 & 0.526 & SVM & rev\({}_{-}\)\(-\)33 & MFCC \\
0.399 & 0.467 & 0.331 & SVM & rev\({}_{-}\)\(-\)33 & RASTA \\
0.456 & 0.444 & 0.468 & SVM & rev\({}_{-}\)\(-\)27 & Merged \\
0.475 & 0.422 & 0.528 & SVM & rev\({}_{-}\)\(-\)27 & MFCC \\
0.398 & 0.467 & 0.329 & SVM & rev\({}_{-}\)\(-\)27 & RASTA \\
0.455 & 0.441 & 0.469 & SVM & rev\({}_{-}\)\(-\)20 & Merged \\
0.480 & 0.430 & 0.531 & SVM & rev\({}_{-}\)\(-\)20 & MFCC \\
0.393 & 0.459 & 0.327 & SVM & rev\({}_{-}\)\(-\)20 & RASTA \\
0.469 & 0.467 & 0.471 & SVM & rev\({}_{-}\)\(-\)12 & Merged \\
0.490 & 0.441 & 0.540 & SVM & rev\({}_{-}\)\(-\)12 & MFCC \\
0.387 & 0.452 & 0.322 & SVM & rev\({}_{-}\)\(-\)12 & RASTA \\ \hline
0.700 & 0.976 & 0.423 & RF & raw & Merged \\
0.683 & 0.979 & 0.388 & RF & raw & MFCC \\
0.658 & 0.912 & 0.403 & RF & raw & RASTA \\
0.466 & 0.452 & 0.480 & RF & noise\({}_{25}\) & Merged \\
0.465 & 0.404 & 0.526 & RF & noise\({}_{25}\) & MFCC \\
0.438 & 0.429 & 0.448 & RF & noise\({}_{25}\) & RASTA \\
0.475 & 0.412 & 0.538 & RF & noise\({}_{20}\) & MFCC \\
0.441 & 0.426 & 0.456 & RF & noise\({}_{20}\) & RASTA \\  
ASTA \\ \hline
0.710 & 0.975 & 0.444 & AB & raw & Merged \\
0.692 & 0.971 & 0.414 & AB & raw & MFCC \\
0.670 & 0.894 & 0.445 & AB & raw & RASTA \\
0.463 & 0.436 & 0.490 & AB & noise\({}_{25}\) & Merged \\
0.468 & 0.391 & 0.545 & AB & noise\({}_{25}\) & MFCC \\
0.439 & 0.370 & 0.507 & AB & noise\({}_{25}\) & RASTA \\
0.465 & 0.432 & 0.498 & AB & noise\({}_{20}\) & Merged \\
0.472 & 0.388 & 0.556 & AB & noise\({}_{20}\) & MFCC \\
0.444 & 0.374 & 0.515 & AB & noise\({}_{20}\) & RASTA \\
0.474 & 0.442 & 0.507 & AB & noise\({}_{15}\) & MFCC \\
0.472 & 0.374 & 0.570 & AB & noise\({}_{15}\) & MFCC \\
0.448 & 0.374 & 0.523 & AB & noise\({}_{15}\) & RASTA \\
0.469 & 0.419 & 0.519 & AB & noise\({}_{10}\) & Merged \\
0.469 & 0.352 & 0.586 & AB & noise\({}_{10}\) & MFCC \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._Let the reader be reminded that all the reported results and graphs are averaged among the 50 speakers. In order to gain a better understanding of the behavior of the system before averaging, Appendix A contains an example of results for a single speaker, also reporting the performances before feature selection and before selecting the top 50 (see Tables A1 and A2). With the main goal of this paper being the assessment of the trends in verification performances when noise and reverb are included in validation data, it is possible to observe sufficiently similar performances among the three different classifiers, and more importantly similar behavior with respect to the decay of accuracy, sensitivity, and specificity with noisy data. Figures 3 and 4 showcase the sensitivity and specificity of the three feature sets (MFCC, RASTA, merged) starting from the performances on the raw dataset, followed by noise, and then reverb. It is easy to observe that the two feature families used, MFCC and RASTA, behave differently on noisy/reverberated test data, with opposite tendencies of worsening sensitivity (class 1 recall) or specificity (class 2 recall), whereas the merged feature family offers a compromise. For ease of visualization, Figures 5-10 are also presented, showcasing the sensitivity (class 1 recall) and specificity (class 2 recall) of a selected single speaker, which makes it easier to observe their different decay tendencies. Figure 3: Sensitivity (class 1 recall) progression for the SVM classifier. This starts from the performances on the raw dataset (no noise), and then shows a decay with progressively noisier data and then progressively more reverberated validation data. Figure 4: Sensitivity (class 1 recall) progression for the SVM classifier. Starts from the performances on the raw dataset (no noise), then shows a decay with progressively noisier data then progressively more reverberated validation data.