## 3 Results and Discussion
### Speech Recognition Results with Packet Losses
Table 2 shows the results on real call center data. Each data set comes from a different call center (of different companies). In all of them, the improvement is quite clear using the _da_model4_, achieving an improvement of 10% in terms of absolute WER in test_call_center_3, compared with the baseline model. Table 2 also shows the 95% confidence intervals of the results obtained according to the method defined in [35]. The confidence intervals of the baseline and the _da_model4_ do not overlap for any of the data sets, which shows that the improvement is statistically significant in all cases. These results strongly suggest that,  
Figure 7: 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 ITU-T G.711 to recover the losses in the _da_model5_* model. _da_model3_ is hidden behind _da_model4_ in the bottom figure. besides producing better results on simulated data, these strategies also provide important improvements in real data. Direct comparison of our results with other results published in the literature is not possible due to differences in the databases used and in our particular approach. In particular, we assume that information about packet losses is not available as is customary in typical PCL settings. A notable exception to this is audio inpainting, which can be applied without knowing the position of the missing audio (blind audio inpainting) [16; 20], but these works do not use data augmentation and speech recognition for evaluation. Despite the difficulties in making comparisons, we have found that our results are in line with a few recently published works. The closest work we have found is [17], where the authors evaluate a PCL system using ASR measured in terms of WER. Although the comparison is not possible since the database is different ([PERSON] read speech in English in [17] vs. [PERSON] conversational telephone speech in Spanish in this work), they found that there is a large improvement in terms of WER when applying data augmentation techniques (no additional information is provided about the data augmentation techniques used), which allows going from a baseline 19.99% WER to a 7.43% WER on data with packet losses. Using the advanced PCL technique proposed in [17], they reach a smaller improvement, lowering the WER to 6.19%. The difference in terms of WER is due to the difficulty of the data (read speech in [17] vs. conversational telephone speech in our case). In any case, and even with these differences, results are similar in the sense that they show the importance and capability of data augmentation techniques to deal with packet losses, and also that, by using a specific PCL approach, results can be further improved (although to a lesser extent). Other related work [31] presents a study of different training approaches for dealing with packet losses in a different task, emotion recognition in speech, showing that matched training (training with the same packet losses degradation as in test) is the best option if that is possible, and data augmentation (including packet losses at different rates in training) is an effective way to improve results when packet losses are present, but the packet loss rate is not known in advance. Although the task is different, again the conclusion is similar to our study. An important difference between our study and the aforementioned studies is that, in our case, we also evaluate our results in real data, while studies such as [17; 31] only present results on artificially introduced packet losses.