  Anomia (word-finding difficulties) is the hallmark of aphasia, an acquired
language disorder most commonly caused by stroke. Assessment of speech
performance using picture naming tasks is a key method for both diagnosis and
monitoring of responses to treatment interventions by people with aphasia
(PWA). Currently, this assessment is conducted manually by speech and language
therapists (SLT). Surprisingly, despite advancements in automatic speech
recognition (ASR) and artificial intelligence with technologies like deep
learning, research on developing automated systems for this task has been
scarce. Here we present NUVA, an utterance verification system incorporating a
deep learning element that classifies 'correct' versus' incorrect' naming
attempts from aphasic stroke patients. When tested on eight native
British-English speaking PWA the system's performance accuracy ranged between
83.6% to 93.6%, with a 10-fold cross-validation mean of 89.5%. This performance
was not only significantly better than a baseline created for this study using
one of the leading commercially available ASRs (Google speech-to-text service)
but also comparable in some instances with two independent SLT ratings for the
same dataset.
