## 2 Related Work in Aphasic Speech Recognition  
ASR is a technological field that has remarkably evolved over the last years from the hand of new methods and architectures based on Deep Neural Networks (DNNs), which are closer to reaching human-like performance in controlled acoustic environments [28; 29; 30; 31; 32]. These improvements have great potential to impact new ASR clinical applications and to develop new e-health solutions [33; 34; 35]. Particularly, ASR technology applied to disordered voices brings the opportunity to implement new assisted and personalized therapies, generate automatic cognitive tests or to develop adapted applications for people with impairments. The first ASR systems for aphasic speech recognition found in the literature were focused on recognizing isolated words within small vocabularies for English [36] and Portuguese [24]. More recently, thanks to the advancements in deep learning speech recognition technologies, new studies achieved up to 90% accuracy on assessing _correct_ versus _incorrect_ naming attempts in controlled utterance verification systems [37]. However, the biggest challenge in the field nowadays is to improve the performance of the continuous recognition of aphasic speech in large vocabularies. To the best of our knowledge, the published works in the task of aphasic continuous speech recognition of large vocabularies only consider English [23; 38; 39] and Cantonese [40] to date. In this sense, the performance and results for these systems widely oscillate depending on the severity level of aphasia, ranging WER from 33 on mildest cases to more than 60 on very severe cases. All these studies employ the same AphasiaBank database [41] as the main corpus for training and evaluation, but they usually differ on the train-test-validation partitions and on the evaluation metrics employed, given that some studies used the Phoneme Error Rate (PER) as its main metric and others employed the Character Error Rate (CER). This decision strongly depends on the configuration and the basic modeling unit used to train their systems (phonemes or characters). Hence, a fair and balanced comparison between systems and technological approaches cannot always be guaranteed. Nonetheless, in some cases, notable improvements can be appreciated between the 52.3 of PER in moderate aphasia test group presented in [25] and the more recent 41.7 of PER reported in [39]. These results seems to be in line with the 38.3 global Syllable Error Rate (SER) reported for the full test set in Cantonese [40], where more than 60% of the test set was composed of mild severity speech data. Regarding technological approaches, previous works focused on developing ASR technology for aphasic speech considering architectures based on hybrid Acoustic Models (AMs) such as Deep Neural Networks and Hidden Markov Models (DNN-HMM) [25], Bidirectional Long Short-Term Memory and Recurrent Neural Models (BLSTM-RNN) [23], and solutions based on Mixture of Experts (MoEs) [39]. More specifically, in the work presented in [38], the authors established the first large-vocabulary continuous speech recognition baseline for English built on the AphasiaBank dataset using a DNN-HMM hybrid AM trained on unseen train-validation-test partitions and by distinguishing performances depending on aphasia severity. They reached PER metrics between 47.41 for mild severity test and 75.81 for very severe test set and reported that appending utterance fixed-length speaker identity vectors (i-vectors) to frame-level acoustic features resulted in PER reductions specially in speakers with more severe levels of aphasia. These results were then improved by using an acoustic modeling method based on a BLSTM-RNN architecture enriched with a trigram language model (LM) estimated on the transcripts of the training audios [23]. In this case, the training of the AM was reinforced with transcribed data from healthy speakers, achieving an improved WER ranging from 33.68 on mild test set to 53.17 on very severe test set.