  Damage to the inferior frontal gyrus (Broca's area) can cause agrammatic
aphasia wherein patients, although able to comprehend, lack the ability to form
complete sentences. This inability leads to communication gaps which cause
difficulties in their daily lives. The usage of assistive devices can help in
mitigating these issues and enable the patients to communicate effectively.
However, due to lack of large scale studies of linguistic deficits in aphasia,
research on such assistive technology is relatively limited. In this work, we
present two contributions that aim to re-initiate research and development in
this field. Firstly, we propose a model that uses linguistic features from
small scale studies on aphasia patients and generates large scale datasets of
synthetic aphasic utterances from grammatically correct datasets. We show that
the mean length of utterance, the noun/verb ratio, and the simple/complex
sentence ratio of our synthetic datasets correspond to the reported features of
aphasic speech. Further, we demonstrate how the synthetic datasets may be
utilized to develop assistive devices for aphasia patients. The pre-trained T5
transformer is fine-tuned using the generated dataset to suggest 5 corrected
sentences given an aphasic utterance as input. We evaluate the efficacy of the
T5 model using the BLEU and cosine semantic similarity scores. Affirming
results with BLEU score of 0.827/1.00 and semantic similarity of 0.904/1.00
were obtained. These results provide a strong foundation for the concept that a
synthetic dataset based on small scale studies on aphasia can be used to
develop effective assistive technology.
