## 4 Conclusions  
In this work, we have tried to mitigate reduced Automatic Speech Recognition (ASR) accuracy when working with low bit-rate codecs and audios including packet losses, by applying data augmentation techniques. During this studio, it has been found that data augmentation can improve robustness in simulated data in these types of scenarios, which can be very common in real call center recordings. On the one hand, the experiments have demonstrated the effectiveness of data augmentation when it is necessary to work with several low bit-rate codecs or audios containing relatively infrequent individual packet losses, achieving a huge improvement compared to the baseline model. On the other hand, on audios containing frequent packet losses or burst packet losses, the data augmentation \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**WER\% [95\% Conf. Int.]**} \\
**Data Set** & **Duration (h)** & **Baseline** & **da\_model4** \\ \hline test\_call\_center\_1 & 3.5 & 39.51[37.89, 41.15] & 32.07[30.63, 33.53] \\ test\_call\_center\_2 & 3 & 40.68[37.16, 44.21] & 34.20[31.30, 37.13] \\ test\_call\_center\_3 & 2 & 47.83[45.77, 49.90] & 37.94[36.26, 39.62] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results in real data obtained from three different call centers obtained with the baseline system and with the best data augmentation strategy (_da_model4_) found using simulated data. Results show the WER and the 95% confidence interval. techniques improve the results in terms of Word Error Rate (WER), but there is still an important gap in performance. For that reason, other alternatives have been analyzed, showing that, when the audios have burst packet losses or many individual packet losses, a combination of a Packet Loss Concealment (PLC) system (such as ITU-T G.711) and a data augmentation strategy can provide further improvements. Finally, we have checked that the best data augmentation strategy found on data including simulated distortions also provides important improvements in real data taken from three different call centers. As future work, considering the very promising results obtained, we plan to continue studying techniques to recover signals containing packet losses without the limitation of exactly knowing where the packet losses are, with the goal to achieve better accuracy in ASR applied to real recordings, and mainly focusing on cases with very frequent losses or burst losses that still have a large impact on ASR performance. The individual contributions are provided as follows. Contributions: Conceptualization, M.P.F.-G. and D.T.T. ; methodology, M.P.F.-G. and D.T.T. ; software, M.P.F.-G.; validation, M.P.F.-G. and D.T.T. ; formal analysis, M.P.F.-G. and D.T.T. ; investigation, M.P.F.-G.; resources, D.T.T. and M.P.F.-G.; data curation, M.P.F.-G.; writing--original draft preparation, M.P.F.-G.; writing--review and editing, D.T.T. ; visualization, M.P.F.-G.; supervision, D.T.T. ; project administration, D.T.T. ; funding acquisition, D.T.T. All authors have read and agreed to the published version of the manuscript. Partly funded by project RTI2018-098091-Bl00, Ministry of Science, Innovation and Universities (Spain) and the European Regional Development Fund (ERDF). Not applicable. Data available statement: Data used in this study are available at [[https://catalog.ldc.upenn.edu/LDC2010S01](https://catalog.ldc.upenn.edu/LDC2010S01)]([https://catalog.ldc.upenn.edu/LDC2010S01](https://catalog.ldc.upenn.edu/LDC2010S01)) with the exception of real call center data, which cannot be made publicly available due to confidentiality constraints. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: