# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 4 Results
### Discussion
As a final point in this discussion, and in order to provide a condensed view of the best obtained results in this work, Figure 8 presents DET curve and EER of the three evaluated methods by themselves (left), and the cascaded application of Log Deep CORAL and pseudo-labelling (right) using the best hyperparameter configuration in terms of DCF metric compared to the unadapted baseline and our submission to the challenge trained using in domain data. Observing the comparative of the single method application (left), it can be seen again that the most promising result in terms of improving the DET curve is obtained by the Log Deep CORAL method, while the improvement of the knowledge distillation method is limited. Focusing on the comparison on the cascaded application of Deep CORAL and pseudo-labelling (right), it can be observed that the DET curve obtained for the cascaded application of both methods (in pink) supports again the hypothesis that both techniques are complementary. Applying the pseudo-labelling strategy on top of the Log Deep CORAL model results in a DET curve with a similar EER value but a performance significantly improved in areas with high FPR and high FNR. With this method we achieve the best DET curve in this paper that, as already presented, allows to decrease even further the DCF metric, which is influenced in a higher way by false negative errors. As an example operating point for comparison, focusing on a FPR value of 1%, the unadapted baseline system shows a FNR value greater than 50%. While the Log Deep CORAL model reduces this value to be approximately 40%, the improvement is much more significant with the combination of both adaptation methods, that reports a FNR value of 25%. Even though there is still a gap between the best obtained results and a model trained using in domain data, in practical scenarios, where no labels are available, experimental results have proved that unsupervised domain adaptation can improve significantly the performance of SAD systems. Best results are obtained using an approach that combines two methods. The application of this solution increases the computational complexity of the adaptation process in training time. However, the increase in complexity introduced by pseudo-labelling is minimal compared to the one already introduced by Log Deep CORAL. The latter implies a training process that computes two covariance matrices for CORAL loss and a classification loss, while the former only requires inference computation on the unlabelled data and then a simple training process with a classification loss. Furthermore,  
Figure 8: DET curve and EER (%) on the FS02 development partition using the three evaluated domain adaptation methods (**left**), and the cascaded application of two of them (**right**) with the best performing hyperparameter configuration in terms of DCF metric compared to the unadapted baseline system and our submission to the challenge trained using in domain data. inference complexity remains the same in all cases as there is only need to compute the final adapted model to obtain SAD predictions.