## 4 Results
### Experiments with RSR-Part I  
In Table 1, we show Equal Error Rate (EER), and National Institute of Standards and Technology (NIST) 2010 minimum detection costs (_DCF10_[27]) results with the different architectures trained on the background subset for female, male and both partitions together. The _DCF10_ measures the cost of detection errors in terms of a weighted sum of false alarm and miss probabilities for some decision threshold, and a priori probability of observing target and non-target speakers. We have found that, as we expected, the first approach with an average reduction mechanism for extracting embeddings does not perform well for this text-dependent speaker verification task. It seems that these types of embeddings do not represent correctly the information to achieve discrimination between the correct speaker and phrase both simultaneously. Furthermore, we show that changing the typical average reduction for a new alignment layer inside the DNN achieves a relative improvement of 92.14% in terms of EER%. In addition, we have evaluated the performance of our system in terms of whether we vary the number of states that are used to model each phrase. Given the length of the phrases in Part I, we observe that it is better to employ a greater number of states to correctly model each phrase. Nevertheless, the EER results were still quite high, so we decided that the results could be improved by training with background and developing subsets together. In Table 2, we can see that, if we use more data to train our systems, we achieve better performance especially in deep architectures \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{4}{c}{**Architecture**} & \multicolumn{4}{c}{**Results (EER%/_DCF10_)**} \\ \hline \multicolumn{2}{c}{**Front-End**} & \multicolumn{2}{c}{**Pooling**} & \multicolumn{2}{c}{**Fenn**} & \multicolumn{2}{c}{**Male**} & \multicolumn{2}{c}{**Fem + Male**} \\ \hline
3C & 3 & _avg_ & \(-\) & 11.20/0.9808 & 12.13/0.9908 & 11.70/0.9887 \\ \hline \(-\) & \(-\) & _HMM_ & 10 & 3.14/0.2733 & 7.18/0.3746 & 6.37/0.4330 \\
1C & 1 & & & 1.75/0.2759 & 2.58/0.3045 & 2.66/0.3465 \\
1C & 3 & & & 1.60/0.2504 & 2.31/0.2926 & 2.29/0.3087 \\
3C & 3 & & & 1.72/0.3150 & 2.78/0.4049 & 2.45/0.3775 \\
4C & 3 & & & 2.31/0.3840 & 3.28/0.5024 & 3.00/0.4596 \\ \hline \(-\) & \(-\) & _HMM_ & 20 & 1.81/0.2186 & 3.00/0.2197 & 3.20/0.3338 \\
1C & 1 & & & 1.42/0.1923 & 1.37/0.1904 & 1.88/0.2954 \\
1C & 3 & & & 1.17/0.1777 & 1.07/1.722 & 1.51/0.2547 \\
3C & 3 & & & 1.14/0.1806 & 1.42/0.2715 & 1.34/0.2356 \\
4C & 3 & & & 1.03/0.1940 & 1.48/0.2712 & 1.34/0.2350 \\ \hline \(-\) & \(-\) & _HMM_ & 40 & 1.35/0.2099 & 1.57/0.1643 & 2.03/0.2886 \\
1C & 1 & & & 1.16/0.1749 & 0.98/0.1495 & 1.56/0.2774 \\
1C & 3 & & & 1.04/0.1696 & **0.77**/**0.1383** & 1.20/0.2335 \\
3C & 3 & & & 0.86/0.1566 & 1.01/0.2094 & 0.98/0.1940 \\
4C & 3 & & & 1.09/0.1975 & 1.27/0.2530 & 1.23/0.2402 \\ \hline \(-\) & \(-\) & _HMM_ & 80 & 1.70/0.2352 & 1.73/0.1814 & 2.25/0.3108 \\
1C & 1 & & & 1.46/0.1971 & 1.19/0.1566 & 1.67/0.2834 \\
1C & 3 & & & 1.27/0.1887 & 0.88/0.1479 & 1.43/0.2595 \\
3C & 3 & & & **0.84**/**0.1533** & 0.88/0.1868 & **0.92**/**0.1872** \\
4C & 3 & & & 1.04/0.2096 & 1.18/0.2708 & 1.17/0.2492 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results on RSR2015-Part I [22] eval set, where Equal Error Rate (EER%) and National Institute of Standards and Technology (NIST) 2010 min cost (_DCF10_) are shown. These results were obtained by training only with a bkg subset and by varying the number of states of the Hidden Markov Model (HMM). The best results are marked in bold. with more than one layer. This improvement is observed for both architectures. This fact remarks on the importance of having a large amount of data to be able to train deep architectures. In addition, we carried out an experiment to illustrate this effect. In Figure 4, we show that, if we increase little by little the amount of data used to train, the results progressively improve.