NUVA: A Naming Utterance Verifier for Aphasia Treatment

David S Barbera,Mark Huckvale,Victoria Fleming,Emily Upton,Henry Coley-Fisher,Catherine Doogan,Ian Shaw,William Latham,Alexander P Leff,Jenny Crinion

doi:10.1016/j.csl.2021.101221

David S Barbera, Mark Huckvale + Show 8 more

Open Access

https://doi.org/10.1016/j.csl.2021.101221

Copy DOI

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.

Highlights

Word retrieval difficulties(aka anomia) are one of the most pervasive symptoms of post-stroke aphasia
Deep learning is a specific kind of machine learning which allows computational models that are composed of multiple layers to learn representations of data with multiple levels of abstraction, and has dramatically improved the state-of-the-art in speech recognition, visual object recognition, and many other domains such as drug discovery and genomics (LeCun et al, 2015)
We report the performance of NUVA at classifying eight aphasic patients' spoken picture naming attempts as 'correct' or 'incorrect' compared to speech and language therapists (SLT) classifications of the same responses

Summary

Introduction

Word retrieval difficulties(aka anomia) are one of the most pervasive symptoms of post-stroke aphasia Despite its' prevalence, few individuals receive a sufficient dose of speech and language therapy to recover maximally. In the UK through the National Health Service, patients receive on average 8-12 hours (Code & Heron, 2003); reviews of speech and language intervention studies have shown superior outcomes for treatments that deliver around 100 hours of therapy (Bhogal et al, 2003; Brady et al, 2016). Assessment of patients' spoken picture naming abilities and practising repetitively over time a range of vocabulary using spoken picture naming tasks, are both integral parts of impairment based anomia treatments (Whitworth et al, 2014)

Objectives

Methods

Results

Discussion

Conclusion