## 3 AphasiaBank Dataset Description and Processing
### Data Processing  
The original data from the AphasiaBank dataset were processed at different acoustic and text levels in order to generate suitable corpora to build the E2E AMs for English and Spanish. The audio files were first extracted from the video recordings and converted to PCM WAV 16 kHz 16-bit format using the open sourced FFmpeg tool [44]. Since the time-codes were provided at the sentence level, the audio was split into correctly aligned audio chunks by using the SoX [45] tookit in order to manage shorter segments for the training of the neural models. In this respect, audio chunks shorter than 0.3 s were discarded to avoid future problems when computing Fourier transform for the spectrograms generation or during the CTC layer alignment in the neural network. Furthermore, audio chunks longer than 30 s were not included in our corpus, with the aim of avoiding memory issues during training.  
Concerning text transcriptions, they originally contained enriched information including not only literally transcribed words and phenomena such as repetitions, sound fragments and phonological transcription but also artifacts such as misalignments or phoneme omissions. In the latter cases, different criteria were applied in order to maintain or definitively discard these phenomena. In the cases where some phonemes were missing but the full word was intelligible, we chose to maintain the entire word, although some of its phonemes may not have been properly pronounced. Moreover, the repetitions of words and semantic mismatches that may occur during the speech were also preserved, since replacing them would not reflect the real speech patterns of the PWA collective. Additionally, it should be remarked that transcriptions also included special symbols representing isolated noises interjections or fillers, including _um_, _uh_, _uhuh_ or _huh_, among others. These symbols included (_FLR_) to represent fillers; (_SPN_) for spoken noises; (_BRTH_) as breathing sounds; and (_LAU_) for laughter. These special symbols were included for training and considered as individual words and characters in the acoustic E2E model. Moreover, contents with empty or mismatched transcriptions were discarded. We illustrated in Table 1 this methodology showing a real example that includes the original and processed transcription from an audio chunk performed by a female moderate non-fluent Broca English speaker.  
Once the cleaning up process was performed, the English corpus included 89.9 h of PWA patients and 51.3 h of healthy controls, whilst the Spanish corpus summed up a total of 1.2 h of PWA speakers and 1 h of healthy controls.  
Since standard partitions for train, validation and test are not provided in the original AphasiaBank dataset, we applied the following criteria to split the processed data.  
For the English corpus, we randomly selected 25% of PWA speakers from each severity level for the test partition, 19% of PWA speakers for the validation test set and the remaining 56% for the training set to create an unseen train/test/validation set. This train partition was called _PWA acoustic set_. In addition, we also created a second training set, which we called _Mixed acoustic set_, by adding data from healthy controls. The configuration of the train/test/validation partitions was mainly thought so that speakers cannot appear simultaneously in more than one subset while the data remained balanced throughout the aphasia severities. Moreover, both validation and test sets were composed only with data from PWA. In this manner, we could compare two different train sets to investigate the usefulness of adding healthy control data in order to improve the performance of the ASR model. Detailed information of the constructed English corpus can be found in Table 2, including the number of subjects, the amount of hours per partition and the levels of aphasia considered.