# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 4 Results
### Deep CORAL Domain Adaptation  
As an additional third alternative to the previously evaluated unsupervised domain adaptation techniques, in the following lines, we evaluate experimentally the feasibility of Deep CORAL and its variations for the SAD task. Following the theoretical explanation provided in Section 2.3.3, we train a new model using the Deep CORAL and Log Deep CORAL techniques using the same experimental setup: the baseline model is used to initialise the new adapted model, that is then fine tuned for 10 epochs using a learning rate decaying exponentially from \(10^{-4}\) to \(10^{-5}\) (10 times smaller than the one used to train the baseline model). CORAL and log CORAL losses are applied only on the final linear layer of the DNN classifier. Final loss term is then computed using cross entropy loss considering the source labels, and the respective CORAL loss weighted by a factor \(\lambda\). As described in the original paper, \(\lambda\) value was chosen so that, at the end of the training, the classification loss and the CORAL loss are in the same order of magnitude. Obtained results using both, Deep and Log Deep CORAL methods, are shown in Table 6 in terms of AUC and EER for three \(\lambda\) values in the same order of magnitude. Figure 6: DET curve and EER (%) on the FS02 development partition using the knowledge distillation domain adaptation setup with various softmax temperature values compared to the baseline system. As observed in Table 6, both Deep CORAL and Log Deep CORAL provide the lowest EER values obtained in this work so far through model adaptation. Best result in terms of EER is obtained using Log Deep CORAL method, with an EER of 5.36%, which results in a 18.17% relative improvement when compared to the unadapted baseline system. Concerning the AUC values observed, obtained results are in line with the best performing system using the pseudo-labelling techniques, showing also better values than the knowledge distillation method. As done in previous experiments, we also report the DET curves in order to observe the behaviour of the adapted systems in all possible operating points. This curve is shown for the Deep CORAL (left) and Log Deep CORAL (right) systems in Figure 7 for multiple values of \(\lambda\) compared to the DET curve of the baseline system. As observed, CORAL-based domain adaptation techniques provide the best improvement in the DET curve so far in this paper. When compared to the baseline system, besides significantly decreasing the EER, an overall improvement can be seen in the DET curve for the areas reporting high FNR values. That is the reason why the AUC value reported increased when compared to the baseline system. Both, Deep CORAL and Log Deep CORAL, seem to be insensitive to \(\lambda\) value, showing a similar performance as long as \(\lambda\) remains in the same order of magnitude. By observing the behaviour of the Log Deep Coral method and the pseudo-label strategies previously characterised, it becomes apparent that both solutions may be complementary. While the Log Deep CORAL DET curve shows no improvement over the baseline for high FPR values, the DET curve for the pseudo-labelling method obtains its best results in that area, making it the one with best performance for high FPR values over the three methods evaluated. This fact suggest that combining both methods, applying them in \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **CORAL Weight** & **AUC (\%)** & **EER (\%)** \\ \hline \multirow{3}{*}{**Deep CORAL**} & \(\lambda\)\(=\) **0.9** & 98.19 & 5.53 \\  & \(\lambda\)\(=\) **1.0** & 98.18 & 5.43 \\  & \(\lambda\)\(=\) **1.1** & 98.25 & 5.53 \\ \hline \multirow{3}{*}{**Log Deep CORAL**} & \(\lambda\)\(=\) **0.9** & 98.26 & 5.36 \\  & \(\lambda\)\(=\) **1.0** & 98.26 & 5.48 \\ \cline{1-1}  & \(\lambda\)\(=\) **1.1** & 98.24 & 5.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: AUC (%) and EER (%) for FS02 development partition using Deep CORAL and Log Deep CORAL domain adaptation setups using various \(\lambda\) weights.