An Utterance Verification System for Word Naming Therapy in Aphasia

Anomia (word finding difficulties) is the hallmark of aphasia an acquired language disorder, most commonly caused by stroke. Assessment of speech performance using pijcture naming tasks is therefore a key method for identification of the disorder and monitoring patient’s response to treatment interventions. Currently, this assessment is conducted manually by speech and language therapists (SLT). Surprisingly, despite advancements in ASR and artificial intelligence with technologies like deep learning, research on developing automated systems for this task has been scarce. Here we present an utterance verification system incorporating a deep learning element that classifies ‘correct’/’incorrect’ naming attempts from aphasic stroke patients. When tested on 8 native British-English speaking aphasics 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 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.