## 5 Evaluation Results and Discussion
### Semi-Supervised ASR Performance for English  
The performances of the different ASR systems developed in this work for aphasic speech recognition in English are reported in Tables 5 and 6 for the CER and WER metrics, respectively. The results are organized by the AM of the ASR system, the acoustic data used to finetune the pre-trained _XLSR-53-uav2 vec2.0_ model, the decoding type, the external LM used for rescoring the initial lattices and the aphasia severity level.  
big differences between AQ level groups are in line with previous publications [23; 38; 39], which PER and WER results are summarized in Table 4.  
At acoustic levels, the best performance was obtained when finetuning the _XLSR-53_ pre-trained model with data from the _Mixed acoustic set_, which included audio content from PWA and healthy controls. In this sense, we report CER and WER reductions of almost a \(\sim\)5% when adding the healthy controls in comparison with using only audios of PWA for training. It implies that the impact of the scarcity of annotated aphasic speech can be partially reduced by incorporating speech from healthy speakers and domains. This finding was explored and applied later on the Spanish dataset.  
Regarding the beam-search decoding using external LMs for rescoring the initial lattices, it was demonstrated that this strategy clearly improves the performance of the speech recognition systems, showing different results depending on the level of severity of aphasia and the type of LM employed. At this point, it is worth remarking that the _Large LM_ does not enhance overall results when comparing with the other LMs, even if it includes more than 803 million extra words, and the special symbols were ignored in order to compute metrics. It suggests that, in this case, the texts from the Librispeech and CommonVoice datasets used for training the LM are too far from the domain sentences of the AphasiaBank dataset. In this manner, the best results are achieved using the _Mixed LM_ model, reaching a 22.3 WER on the mild severity level group, a 35.1 WER over the moderate subset, a 34.1 WER for severe PWA and 55.5 WER on very severe cases. Overall, this LM reported improvements of \(\sim\)2% in comparison with using the _In-domain LM_ and \(\sim\)7% when comparing to greedy decoding.  
The results obtained show that, despite the great differences in the quality of pronunciation in speakers from mild to very severe groups, the semi-supervised learning method applied in this work is able to generalize the learning of contextualized speech representations of a very diverse type of speech, improving the ASR performance for all cases. This strategy is again demonstrated in Section 5.2 for the Spanish language. Finally, although a fair and well-balanced comparison of these results cannot be fully established with the ones published in previous studies (see Table 4) considering the differences in the modeling units (character versus phoneme) and the possible mismatch in data partitions, the results provided in this work for the English language (Tables 5 and 6) constitute a significant improvement in the quality of aphasic speech recognition systems tested to date on the AphasiaBank dataset.