NUVA: A Naming Utterance Verifier for Aphasia Treatment. (September 2021)

• Record Type: Journal Article
• Authors: Barbera, David S.; Huckvale, Mark; Fleming, Victoria; Upton, Emily; Coley-Fisher, Henry; Doogan, Catherine; Shaw, Ian; Latham, William; Leff, Alexander P.; Crinion, Jenny
• Is Part Of: Computer speech & language. Volume 69(2021)
• Publication Date: 2021-09
• Subjects: Speech disorders -- Word naming -- Aphasia -- Anomia -- Speech recognition -- Dynamic Time Warping; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2021.101221
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: NUVA automatically assesses online word naming attempts in aphasia therapy. Significantly more accurate and faster than leading commercial speech recognition. Accuracies between 83.6% and 93.6% validate use in clinical research. 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.