NUVA: A Naming Utterance Verifier for Aphasia Treatment

TL;DR

NUVA is an AI-based system that automatically verifies correct word retrieval in aphasia patients, achieving high accuracy and outperforming commercial speech recognition tools, thus aiding speech therapy assessments.

Contribution

This paper introduces NUVA, a novel deep learning system for automated verification of naming attempts in aphasia, addressing a gap in AI applications for speech therapy assessment.

Findings

NUVA achieved 83.6% to 93.6% accuracy on patient data.

NUVA outperformed commercial speech recognition baseline.

NUVA's performance was comparable to expert SLT ratings.

Abstract

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.