NUVA: A Naming Utterance Verifier for Aphasia Treatment
| Autor: | Ian Shaw, Mark Huckvale, Emily Upton, Victoria Fleming, David Sabate Barbera, Alexander P. Leff, William Latham, Henry Coley-Fisher, Catherine Doogan, Jenny Crinion |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Dynamic time warping Computer science Anomia 02 engineering and technology Speech recognition computer.software_genre 01 natural sciences Article Theoretical Computer Science Task (project management) Aphasia 0103 physical sciences 0202 electrical engineering electronic engineering information engineering medicine Aphasia Treatment Language disorder 010301 acoustics Stroke Dynamic Time Warping Speech disorders Computer Science - Computation and Language business.industry Deep learning 020206 networking & telecommunications Word naming medicine.disease 3. Good health Human-Computer Interaction Artificial intelligence medicine.symptom business computer Computation and Language (cs.CL) Software Natural language processing Utterance |
| Zdroj: | Computer Speech & Language |
| ISSN: | 0885-2308 |
| Popis: | 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. Comment: Under review |
| Databáze: | OpenAIRE |
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