# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 4 Results  
### Baseline System  
As starting point for the experimentation, the main objective is to obtain a baseline system so that further experiments could be compared against. This baseline model is the one considered as the unadapted model, trained only with out of domain data. This model is fine tuned in the following experiments using the methods previously explained in order to obtain a model adapted to the unseen domain. The experimental setup is built upon the description provided in Section 3. Concerning the details of the optimisation process, adam optimiser is used with a learning rate that decays exponentially from \(10^{-3}\) to \(10^{-4}\) during 20 epochs. Minibatch size is chosen to maximise the GPU memory usage. Model selection is performed by choosing the best performing model in terms of frame classification accuracy on the validation subset. Unless stated otherwise, these optimisation details are common among all the following experiments described in this paper.  
Results for the baseline system trained on broadcast, telephonic and meetings domains in terms of AUC and EER can be observed in Table 2. Additionally, we also report the results of one of our submissions to the original FS02 challenge [23] that was trained using the same experimental setup but with data coming from the training partition provided for the challenge. This result is presented in order to provide an upper bound for the neural architecture performance in case it was trained with in domain data.  
First, we report results for the three domains that the SAD baseline system has seen in the training process. In general terms, it can be observed that competitive results are obtained on the three domains shown, with EER values in the range 6 to 7%. Focusing on the obtained results for FS02 development partition, it can be observed that, as expected, the baseline system underperforms when compared to the challenge submission trained within domain data. Particularly, a drop in performance close to 50% can be observed in terms of EER. In the following subsections, we aim to fill the gap between the baseline model and the upper bound provided by the system submitted to FS challenge by using unsupervised domain adaptation techniques.  
Results shown in Table 2 are complemented with those presented in Figure 4. In this figure, we show the DET curve and EER for the baseline system and the challenge submission system on FS02 development partition.  
As it can be observed, a similar drop in performance measured by the EER metric is applicable to all points in DET curve. Furthermore, the baseline system tends to provide higher FNR values, whereas the challenge submission curve shows a relatively constant slope in all the displayed range. As a point for comparison, for FPR = 1%, the challenge submission provides FNR = 10% while the baseline unadapted system yields FNR greater than 50%.  
Now that the baseline system has been appropriately characterised and set in context, in the following subsections we present the results for the three domain adaptation techniques described in the paper.