## 3 Results and Discussion
### Speech Recognition Results with Packet Losses
Figure 3: Test results comparing the baseline with data augmentation systems for single packet losses (**a**) and burst packet losses (**b**) for different packet loss probabilities. _da_model3_ is hidden behind _da_model4_ in the bottom figure. only the _da_model5_ strategy, which has been trained with speech previously processed by the ITU-T G.711 PLC system, provides clear improvements. In fact, the difference between _da_model5_ and any of the other models is statistically significant (\(POI>0.99\)) in all cases with packet losses. The rest of the data augmentation strategies have been trained with speech not processed by the PLC system, and therefore fail to provide consistent improvements on speech processed with this PLC system. Figure 4: Development results comparing the baseline with data augmentation systems for single-packet losses (**a**) and burst packet losses (**b**) for different packet loss probabilities and applying ITU-T G.711 PLC system to recover the losses in development data. Figures 4 and 5 were not fair comparisons since all models except _da_model5_ suffer from a large mismatch between training and evaluation data. To show a fairer comparison among the different data augmentation strategies, Figures 6 and 7 show the results obtained with all models on the original development and test data sets and the results obtained with _da_model5_ both when evaluated on the original data sets (denoted as _da_model5_ in the figures) and when evaluated on data processed by the ITU-T G.711 PLC system (denoted as _da_model5*_ in the figures). These experiments show that, for individual packet losses, performances achieved by _da_model2_ (the best performing system) and _da_model5*_ are quite close, although performance is slightly worse for _da_model5*_. The difference in performance between these two models is statistically significant (_POI_ \(>\) 0.99) only for packet loss rates of 15% and 20% in test and development data. However, when there are burst packet losses, the accuracy is clearly better for _da_model5*_. In this case, all differences between _da_model5*_ and the second best performing system, _da_model3_ are statistically significant (_POI_ \(>\) 0.99) in all cases with a packet loss rate over 5%, while, for a 5% packet loss rate, the _POI_ is 0.9696  
Figure 5: Test results comparing the baseline with data augmentation systems for single packet losses (**a**) and burst packet losses (**b**) for different packet loss probabilities and applying a ITU-T G.711 PLC system to recover the losses in test data. in test data and 0.9887 in development data, showing a high probability of improvement. Therefore, our results show that combining a standard packet loss concealment system such as the one included in ITU-T G.711 standard and a data augmentation strategy provides additional benefits over just using data augmentation. Figure 6: Development results comparing the baseline with data augmentation systems for single packet losses (**a**) and burst packet losses (**b**) for different packet loss probabilities and applying ITU-T G.711 to recover the losses in the _da_model5_* model. _da_model3_ is hidden behind _da_model4_ in the bottom figure. Thus far, all the experiments were performed on data with simulated degradations. At this point, our goal is to corroborate the effectiveness of these data augmentation strategies in real data. An experiment using real call-center data was designed to compare results using the baseline model (which only included data augmentation based on speed perturbation) and _da_model4_, the best data augmentation strategy found in data with simulated degradations without the packet loss concealment system. It is worth mentioning that the ITU-T G.711 PLC system cannot be applied to this real data because we do not have information about which packets were lost. To do these experiments, the language model and the lexicon have been adapted to the call center data and its particularities, being exactly the same for the baseline and the _da_model4_ data augmentation strategy.