## 3 Results and Discussion
### Speech Recognition Results with Packet Losses  
Figures 2 and 3 show the results obtained with the baseline system and the systems trained with several data augmentation techniques in development and test data sets, respectively, for several packet loss percentages of individual packet losses and burst packet losses. The _da_model2_, which only contains data augmentation with single packet losses, obtains the best results in scenarios with only individual losses. There is a similar situation with _da_model3_, a data augmentation strategy that only considers burst packet losses, obtaining the best results in the scenario with burst packet losses. With the aim to apply these techniques in real world scenarios where both types of losses can occur, the _da_model4_ data augmentation strategy, which considers both single and burst losses, is proposed as a more realistic model. The results obtained with this strategy are close to the best results reached by the rest of the models in both situations, individual and burst packet losses. Therefore, this approach provides a large improvement in terms of robustness against both individual and burst packet losses. The statistical significance of the differences shown in Figures 2 and 3 have been analyzed by estimating the probability of improvement (_POI_) of  
Figure 1: Baseline vs. different data augmentation systems in test (**top**) and development (**bottom**) sets in original format and with three different low bit rate codecs (MP3-8 Kbit/s, MP3-16 KBit/s, and GSM-FR). one method over the others. For the case of individual losses, both _da_model2_ and _da_model4_ are better than the rest of the models (with \(POI>0.99\) in all cases with packet losses). Comparing both approaches, the differences between _da_model2_ and _da_model4_ are only statistically significant (\(POI>0.99\)) for packet loss rates of 20% in test data and 15% and 20% in development data. For the case of burst packet losses, both _da_model3_ and _da_model4_ are better than the rest of the models (with \(POI>0.99\) in all cases with packet losses, except for a 5% packet loss rate in development data, for which \(POI=0.8507\) between _da_model4_ and _da_model5_). Comparing both approaches, the differences between _da_model3_ and _da_model4_ are not statistically significant (\(POI<0.99\)) in all cases (the maximum is \(POI=0.9659\) in test data with a 20% packet loss rate. Figure 2: Development 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. Experiments so far show that, when there are individual losses and the probability is low (5% or 10 %), Automatic Speech Recognition (ASR) results including a _da_model4_ data augmentation strategy are not far from the baseline results without packet losses (but we must highlight that the difference between the baseline result and any result including some percentage of packet losses with any data augmentation strategy is still statistically significant with \(POI>0.99\)). However, for the rest of the scenarios (more individual packet losses or burst packet losses), there is still a significant gap in performance compared to results without packet losses, even after having obtained large improvements from the _da_model4_ data augmentation strategy. Obviously, these larger differences are also statistically significant. This gap in performance reaches a maximum of about 7% in terms of absolute WER in the worst scenario considered. To try to mitigate these effects even more, we have applied the ITU-T G.711 Packet Loss Concealment (PLC) standard to the development and test data sets to analyze how the different data augmentation strategies behave. Figures 4 and 5 show the results obtained. In these figures, it is quite clear that the performance of the baseline is very similar to the baseline.