## 6 Conclusions and Future Work  
In this work, we show that semi-supervised learning methods applied to the ASR are promising solutions for improving the performance on aphasic speech recognition. Moreover, we set new benchmarks for the English AphasiaBank dataset, and we performed the first study for the Spanish language. The acoustic data for training were augmented using a mix of data from PWA and healthy controls, demonstrating that this strategy considerably improves the performance. This benefit was boosted for the case of Spanish, which included less than one hour of available aphasic speech data. These results open the door to improve ASR systems for people with aphasia and other clinical speech pathologies, or even simply to make speech recognition engines available for those languages with few annotated and available data. As future work, it would be interesting to check if the performance of the systems could be improve by considering some other learning rate schedulers, by tuning the SpecAugment parameters or by considering other hyperparameters configurations. Moreover,whether the results could be enhanced by fine-tuning specific models for each level of aphasia severity should be evaluated, as speakers in each group probably perform similar speech and acoustic patterns. Another strategy worth studying would be to train AMs by directly removing the special symbols and then rescoring with an external _Large LM_. In any case, this point should be considered depending on the application, since special symbol information can be important for clinical practice but irrelevant for voice assistants. Furthermore, AMs may even be finetuned relative to individual patient speech by using Federated Learning approaches [54]. Finally, future studies should be also focused on extending this semisupervised learning method to other languages where no benchmarks on aphasic speech recognition voices has been reported, probably due to the scarcity of annotated data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**AM** & **Acoustic Data** & **Decoding** & **LM** & **CER** & **WER** \\ \hline XLSR-53-wav2 vec2.0 & PWA acoustic set & Greedy & - & 25.8 & 49.8 \\ \hline XLSR-53-wav2 vec2.0 & Mixed acoustic set & Greedy & - & **24.1** & 45.3 \\ \hline XLSR-53-wav2 vec2.0 & Mixed acoustic set & Beam & Large & 24.8 & **42.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CER and WER metrics on Spanish test set of AphasiaBank where there was no clinical information on the severity of aphasia of participants. The _Mixed acoustic set_ combines data from PWA and one hour of clean speech from the CommonVoice dataset. The _Large LM_ was trained with texts from the Common Voice dataset and digital news from the generic domain. They do not include special symbols. In addition, this technology should be tested in clinical practice, as well as in real medical environments and applications. Conceptualization, I.G.T. and A.A.; methodology, I.G.T., M.R. and A.A.; software, I.G.T. and M.R. ; validation, I.G.T. and M.R. ; formal analysis, I.G.T. and A.A.; investigation, I.G.T., M.R. and A.A.; resources, I.G.T. and M.R. ; data curation, M.R. ; writing--original draft preparation, I.G.T. ; writing--review and editing, I.G.T. and A.A.; visualization, A.A.; supervision, I.G.T. and A.A.; project administration, I.G.T. ; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors would like to acknowledge AphasiaBank and, especially, to the people who contributed to it. The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results. The following abbreviations are used in this manuscript: