# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 4 Results
### Cascaded Application of Domain Adaptation Methods  
In view of results presented in previous subsections, this final experiment evaluates the possibility of applying two domain adaptation methods in a cascaded setup in order to improve even further the results on the new unseen domain. By combining the capabilities of CORAL based adaptation to obtain an overall boost in performance and the pseudo-label adaptation to obtain a DET curve with a constant slope, we use both of them in a cascaded setup. The baseline model is first adapted using the Log Deep CORAL method. Then the adapted model is used to extract a new set of pseudo-labels, which are later used to obtain a final model using both training strategies described in previous experiments, either training a new model from scratch, or fine-tuning the previous model. Table 7 shows an objective evaluation of the pseudo-labels obtained via the Log Deep CORAL model in terms of AUC, EER, FPR and FNR for the same three possible operating points considered in previous experiments.  
As expected, we can observe that the overall quality of the pseudo-labels has improved when compared to those obtained using the baseline model (see Table 3). Improvements obtained are in line with the ones presented on the FS Development subset when using the Log Deep CORAL method, with the EER metric decreasing from 7.28 to 5.63%. By using this new set of pseudo-labels the adaptation process is performed feeding the neural network with an approximately 11% of wrong labels distributed according to the three operating points shown. Obtained results using this new set of pseudo-labels are described in Table 8 in terms of AUC and EER for each possible operating point and for both training strategies described.  
The previously observed behaviour of the pseudo-labelling method is repeated in this new experiment. In general terms, compared to Log Deep CORAL model (\(\text{AUC}=98.25\%\), \(\text{EER}=5.48\%\)), it can be seen that pseudo-labelling strategies provide an improvement on the AUC metric while maintaining a similar EER value. As a matter of fact, all the AUC values reported outperform those from previous experiments, with the best case scenario of \(\text{AUC}=98.86\%\) obtained by training a new model from scratch using low FPR pseudo-labels. Concerning the training strategies considered, experimental results suggest that no significant difference can be found between training a new model from scratch or fine tuning the previous stage model. In terms of operating point, the EER value obtained using  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **AUC (\%)** & **EER (\%)** & **Operating Point** & **FPR (\%)** & **FNR (\%)** \\ \hline \multirow{3}{*}{FS02–Train} & \multirow{3}{*}{98.26} & \multirow{3}{*}{5.63} & Low FPR & 4.55 & 7.55 \\  & & & Balanced & 5.70 & 5.54 \\ \cline{1-1}  & & & Low FNR & 7.27 & 3.78 \\ \hline \hline \end{tabular}
\end{table}
Table 7: AUC, EER, FPR and FNR on three operating points for the pseudo-labels obtained using the best performing model adapted through Log Deep CORAL method on FS02 training partition.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Pseudo-Labels**} & \multicolumn{2}{c}{**From Scratch**} & \multicolumn{2}{c}{**Fine Tuning**} \\ \cline{2-5}  & **AUC (\%)** & **EER (\%)** & **AUC (\%)** & **EER (\%)** \\ \hline Low FPR & 98.86 & 5.21 & 98.85 & 5.34 \\ Balanced & 98.81 & 5.50 & 98.83 & 5.49 \\ Low FNR & 98.75 & 5.67 & 98.85 & 5.51 \\ \hline \hline \end{tabular}
\end{table}
Table 8: AUC (%) and EER (%) for FS02 development partition using pseudo-label domain transfer setup training a new neural network from scratch and fine tuning the best performing model adapted using Log Deep CORAL method.  
low FPR is slightly lower for both training strategies, however this difference becomes insignificant when considering the AUC metric.