## 4 Results
### Experiments with RSR-Part I
In addition to that, we can see in Figure 4b that the alignment mechanism makes the system more robust to training data size. To make this representation, we have used the alignment mechanism with 40 states. In addition, the shaded areas of Figure 4 depict the standard deviation of the EER values obtained from each system trained three times. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{3}{c}{**Architecture**} & \multicolumn{4}{c}{**Results (EER\%/DCF10)**} \\ \hline \multicolumn{3}{c}{**Front-End**} & \multicolumn{3}{c}{**Pooling**} & \multicolumn{1}{c}{**Fem**} & \multicolumn{1}{c}{**Male**} & \multicolumn{1}{c}{**Fem+Male**} \\
**Layers** & **Kernel** & **Type** & **States** & **Fem** & **Male** & **Fem+Male** \\ \hline
3C & 3 & _avg_ & \(-\) & 9.11/0.9585 & 8.66/0.9626 & 8.87/0.9629 \\ \hline \(-\) & \(-\) & _HMM_ & 10 & 3.14/0.2733 & 7.18/0.3746 & 6.37/0.4330 \\
1C & 1 & & & 1.66/0.2522 & 2.30/0.2654 & 2.49/0.3349 \\
1C & 3 & & & 1.43/0.2267 & 2.01/0.2561 & 2.13/0.2979 \\
3C & 3 & & & 1.29/0.2342 & 2.08/0.2870 & 1.82/0.2746 \\
4C & 3 & & & 1.56/0.2853 & 2.41/0.3828 & 2.12/0.3445 \\ \hline \(-\) & \(-\) & _HMM_ & 20 & 1.81/0.2186 & 3.00/0.2197 & 3.20/0.3338 \\
1C & 1 & & & 1.30/0.1806 & 1.29/0.1625 & 1.79/0.2787 \\
1C & 3 & & & 1.15/0.1675 & 1.05/0.1397 & 1.51/0.2392 \\
3C & 3 & & & 0.83/0.1329 & 1.11/0.1897 & 1.03/0.1795 \\
4C & 3 & & & 0.99/0.1894 & 1.32/0.2548 & 1.22/0.2375 \\ \hline \(-\) & \(-\) & _HMM_ & 40 & 1.35/0.2099 & 1.57/0.1643 & 2.03/0.2886 \\
1C & 1 & & & 1.17/0.1822 & 0.98/0.1441 & 1.55/0.2673 \\
1C & 3 & & & 1.07/0.1521 & 0.78/**0.1175** & 1.24/0.2245 \\
3C & 3 & & & **0.59/0.1046** & 0.71/0.1570 & 0.73/**0.1418** \\
4C & 3 & & & 0.74/0.1584 & 0.89/0.2153 & 0.86/0.1945 \\ \hline \(-\) & \(-\) & _HMM_ & 80 & 1.70/0.2352 & 1.73/0.1814 & 2.25/0.3108 \\
1C & 1 & & & 1.32/0.2163 & 1.00/0.1367 & 1.60/0.2969 \\
1C & 3 & & & 1.17/0.1776 & 0.78/0.1289 & 1.34/0.2521 \\
3C & 3 & & & 0.65/0.1112 & **0.61**/0.1539 & **0.68**/0.1444 \\
4C & 3 & & & 0.80/0.1648 & 0.83/0.1980 & **0.84**/0.1871 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results on RSR2015-Part I [22] eval set, showing EER% and NIST 2010 min cost (_DCF10_). These results were obtained by training with bkg+dev subsets and by varying the number of states of the HMM. The best results are marked in bold. Figure 4: Results of EER% varying train percentage where standard deviation is shown only for both gender independent results. (**a**) average embeddings; (**b**) supervectors. For illustrative purposes, we also represent our high-dimensional supervectors in a two-dimensional space using T-Distributed Stochastic Neighbor Embedding (t-SNE) [28], which preserves distances in a small dimension space. The axes in this representation just define the two-dimensional space to project the high-dimensional space, so they do not have a meaning in terms of units. In Figure 5a, we show this representation for the architecture that uses the time average to extract the embeddings, while, in Figure 5b, we represent the supervectors of our best system. We can see that the second system is able to cluster the examples from the same person, whereas the first method is not able to cluster them together. On the other hand, in both representations, data are auto-organized to show on one side examples from female identities and on the other side examples from male identities. Furthermore, we illustrate in Figure 6 the same representation as in Figure 5. However, in this case, we represent the embeddings and the supervectors of the thirty phrases from female identities. With this depiction, we verify something that we had already observed in the previous verification experiments since the embeddings obtained by using a global average pooling layer are not able to separate between the same identity with different phrases and the same identity with the same phrase, which is the base of a text-dependent speaker verification task.