# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 4 Results
### Discussion  
Once all the results for the unsupervised domain adaptation methods have been described, in this subsection we aim to provide a brief discussion on its behaviour, setting them in the context of the original FS challenge and using the original DCF metric in order to obtain a performance comparison. Table 9 presents a summary of the best obtained results using all methods explored (pseudo-labels, KD, Deep CORAL), and the cascaded application of both of them in terms of AUC, EER and DCF metrics as used in the FS challenge. We also report the relative improvement obtained in DCF metric compared to the unadapted baseline system. For comparison, we present the challenge baseline result provided by the FS challenge organisation [72], and our submission to the challenge trained using in domain data. Concerning the results of the pseudo-labelling strategy, we can observe a relative improvement in DCF metric between 11% and 12% when compared to the unadapted baseline system. This improvement comes mainly from the correction made to the DET curve, with those systems showing a behaviour with more constant slope. This means that systems adapted using this method show a better performance in areas with high FPR or high FNR while, at the same time, EER remains very similar to that of the baseline system. The knowledge distillation systems offer the lowest improvement in DCF of all methods evaluated. Even though its configuration with high softmax temperature still shows a non-neglectable 5.79% relative improvement compared to the baseline system, this solution shows limited applications. DET curve is really similar to the one obtained by the baseline system, with limited improvement in AUC. Finally, CORAL-based methods show one of the most promising results of this study. The best hyperparameter configuration using Log Deep CORAL achieves a DCF relative improvement of 13.23% compared to the baseline system. These results shows the lowest EER value in this paper in the case of the application of a single technique, while also reporting an overall improvement for the full DET curve. Best results in terms of DCF are obtained by applying Log Deep CORAL and pseudo-labelling in a cascaded setup. In the case of training a new model from scratch using pseudo-labels from the previous step, DCF is 3.65%, which is equivalent to 24.59% relative improvement compared to the unadapted baseline system. Furthermore, experimental \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **AUC (\%)** & **EER (\%)** & **DCF (\%)** & **Rel. Improv. (\%)** \\ \hline Baseline & 97.57 & 6.55 & 4.84 & - \\ \hline Pseudo-labels (fine tune) & 98.03 & 6.20 & 4.25 & 12.19 \\ Pseudo-labels (scratch) & 98.21 & 6.46 & 4.31 & 10.95 \\ \hline Knowledge distillation (\(T=1\)) & 97.79 & 6.41 & 4.78 & 1.24 \\ Knowledge distillation (\(T=50\)) & 97.86 & 6.02 & 4.56 & 5.79 \\ \hline Deep CORAL (\(\lambda=1.1\)) & 98.25 & 5.53 & 4.26 & 11.98 \\ Log Deep CORAL (\(\lambda=1.0\)) & **98.25** & **5.48** & **4.20** & **13.23** \\ \hline Log CORAL + PL (fine tune) & 98.85 & 5.34 & 3.67 & 24.17 \\ Log CORAL + PL (scratch) & **98.86** & **5.21** & **3.65** & **24.59** \\ \hline Challenge baseline & - & - & 12.50 & - \\ Challenge submission & 99.56 & 3.28 & 2.56 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 9: AUC (%), EER (%), DCF (%) and DCF relative improvement (%) over the unadapted baseline model on the FS02 development partition using the three evaluated domain adaptation methods, and the cascaded application of two of them with the best performing hyperparameter configuration in terms of DCF metric compared to the original challenge baseline and our submission to the challenge trained using in domain data. results confirm that both methods are complementary. As shown, the total relative improvement with both techniques is equivalent to the sum of relative improvements when used separately.