# Multimodal Diarization Systems by Training Enrollment Models as Identity Representations +
## 7 Results
### Effect of aDCF Parameters \(\gamma\), \(\beta\) for Training Face Enrollment Models  
A second set of experiments was performed to observe the effect of training with different aDCF loss parameters. In Table 2 and Figure 4, we observe that the system performance improves by only adjusting the aDCF parameters without modifying the threshold values or the tracking and identity assignment algorithm for the reference number of training epochs. As reference number of epochs, we considered 800 epochs as it is the number initially used in the previous set of experiments. In view of this result, we explored, in depth, the behavior of the training enrollment models for the different configurations of parameters and number of epochs. As a result of this sweep, we obtained that the original  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Back-End** & **Nontarget Examples** & **DER\%** \\ \hline Average Embedding & \(-\) & 80.16\% \\ \hline Enrollment Models & 57 & 61.79\% \\  & 3302 & **56.86\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results on RTVE 2020 Multimodal Diarization test set, showing DER%. These results were obtained to compare the back-end approach proposed and the cosine baseline. The best result is marked in bold.  
parameter configuration, \(\gamma=0.75\) and \(\beta=0.25\), has still room for improvement, and training for 1200 epochs achieves the best result without modifying any other parameter. Moreover, we note that when giving a higher cost relevance (\(\beta\)) to the probability of misses during training with aDCF loss function, regardless of the number of epochs, the results are worse in all situations.  
In addition to the above results, we analyzed the behavior of the three types of errors that compose the DER% metric when the different possible system configurations are used for the same number of training epochs. As we can see in Table 3 and Figure 5, giving a higher cost relevance to the probability of false alarms (\(\gamma\)) in the aDCF loss results in a lower number of false alarms in the decomposition of the DER%, while the number of misses is higher than in the other two configurations. On the other hand, the same trend can be seen in reverse when the relevance term (\(\beta\)) is higher for the probability of misses.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(\gamma\) & \(\beta\) & **MISS** & **FA** & **ID** & **DER** \\ \hline
0.75 & 0.25 & 32.60\% & **12.80\%** & **8.70\%** & **54.07\%** \\
0.50 & 0.50 & 30.70\% & 16.00\% & 9.80\% & 56.47\% \\
0.25 & 0.75 & **28.30\%** & 19.80\% & 12.00\% & 60.01\% \\ \hline \end{tabular}
\end{table}
Table 3: Experimental results of RTVE 2020 Multimodal Diarization test set, showing DER% and decomposition of DER% results in miss (MISS), false alarm (FA), and identity (ID) errors. These results were obtained by sweeping the parameters of the aDCF loss function. The best results are marked in bold.  
Figure 4: Evolution of DER% results as a function of the different parameter configurations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(\gamma\) & \(\beta\) & **600 Epochs** & **800 Epochs** & **1000 Epochs** & **1200 Epochs** & **1400 Epochs** \\ \hline
0.75 & 0.25 & 62.56\% & 56.86\% & **55.32\%** & **54.07\%** & **55.91\%** \\
0.50 & 0.50 & **55.92\%** & **55.89\%** & 56.02\% & 56.47\% & 57.16\% \\
0.25 & 0.75 & 58.30\% & 59.29\% & 59.58\% & 60.01\% & 60.02\% \\ \hline \end{tabular}
\end{table}
Table 2: Experimental results of RTVE 2020 Multimodal Diarization test set, showing DER%. These results were obtained by sweeping of the parameters of aDCF loss function and by different number of training epochs. The best results are marked in bold.