# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 4 Results
### Pseudo-Label Domain Adaptation  
As described in Section 2.3.1, pseudo-labelling is a method traditionally used to alleviate the need for labelled data. In the following experiments, we use it in order to adapt a model to a new unseen domain. The first step needed to perform this technique is to obtain a new set of pseudo-labels for the target data. To do so, we run the FS02 training partition through the previously obtained baseline SAD model and store the speech scores, that are then thresholded accordingly to obtain the final pseudo-labels. In the next step, those pseudo-labels are considered as the ground truth for the target data and used to train a new model in a supervised way. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} 6.55 \\ 99.56 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: AUC (%) and EER (%) on three in domain datasets and FS02 development partition compared to a system submitted to the challenge raised using only data from FS challenge. Figure 4: DET curve and EER (%) on FS02 development partition comparing a baseline system trained using out of domain data and our submission to the challenge trained using in domain data. Even though labels for FS02 training partition data are not used in this paper to train any model, those labels are still available and can be used to obtain an objective evaluation of the pseudo-labels obtained via the baseline model. This evaluation is shown in Table 3 in terms of AUC and EER. Furthermore, as this method relies heavily on the operating point chosen for the pseudo-labels, we also report FPR and FNR for three operating points: one with low FPR, one with balanced FPR and FNR, and one with low FNR. By using these operating points, three sets of pseudo-labels are obtained. Experiments are performed separately for each set of pseudo-labels in order to observe the influence of the operating point in this domain adaptation strategy. As it can be observed, the values for AUC and EER are in line with those obtained with the baseline model on the development partition, yet the EER is slightly greater for the training partition. Concerning the operating points considered, it should be noted that, in general terms, by using these pseudo-labels the neural network is dealing with an approximately 15% of wrong labels in the adaptation process. Once pseudo-labels have been obtained using the baseline model, several alternatives can be used to obtain a new adapted model. In this paper, we explore two of those alternatives. On the one hand, we train a new model from scratch using the same experimental setup as the one described for the baseline model but using FS02 training partition audio and the obtained pseudo-labels as ground-truth. On the other hand, we also evaluate the possibility of fine-tuning the baseline model via the obtained pseudo-labels for the FS02 training partition, using a learning rate ten times smaller than the one used in the original training process. Table 4 describes the obtained results in terms of AUC and EER for each possible operating point and for both training strategies. When compared to the unadapted baseline system, it can be observed that the results presented using the pseudo-labelling method share two common characteristics: a minor improvement in terms of AUC metrics, while the EER remains similar to the one reported in the baseline system. No significant difference can be observed between the two training strategies presented. Concerning the operating points for the pseudo-labels, the low FPR operating point yields the lowest EER for both training strategies, while at the same time also reporting the lowest AUC values. In order to further understand the behaviour of the pseudo-labelling strategy, Figure 5 shows the DET curve and EER on the FS02 development partition for the baseline system and the systems trained using pseudo-labelling domain adaptation methods. From Figure 5, it can be seen that the pseudo-labelling technique provides no significant improvement in EER values.