Evaluating Fluency in Aphasia: Fluency Scales, Trichotomous Judgements, or Machine Learning-Reference-Cited by-同舟云学术

Evaluating Fluency in Aphasia: Fluency Scales, Trichotomous Judgements, or Machine Learning

Published:2023-02-06 Issue:1 Volume:38 Page:168-180
ISSN:0268-7038
Container-title:Aphasiology
language:en
Short-container-title:Aphasiology

Author:

Metu Jeet¹,Kotha Vishal²,Hillis Argye E.³^ORCID

Affiliation:

1. Rock Ridge High School

2. Thomas Jefferson High School for Science and Technology

3. Departments of Neurology and Physical Medicine & Rehabilitation, Johns Hopkins University School of Medicine, and Cognitive Science, Johns Hopkins University, Baltimore, MD 21287

Funder

the National Institute on Deafness and Other Communication Disorders)

Publisher

Informa UK Limited

Subject

LPN and LVN,Neurology (clinical),Neurology,Linguistics and Language,Developmental and Educational Psychology,Language and Linguistics,Otorhinolaryngology

Link

https://www.tandfonline.com/doi/pdf/10.1080/02687038.2023.2171261

Reference21 articles.

1. Subject Description and Generality of Results in Experiments with Aphasic Adults

2. The logic of neuropsychological research and the problem of patient classification in aphasia

3. Understanding Fluency in Aphasia;Clough S;Frontiers in Human Neuroscience,2018

4. Fluent or nonfluent? Part A. Underlying contributors to categorical classifications of fluency in aphasia

5. Aphasia type, age and cerebral infarct localisation

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1. AI-assisted assessment and treatment of aphasia: a review;Frontiers in Public Health;2024-08-29

2. A scoping review of transcription‐less practices for analysis of aphasic discourse and implications for future research;International Journal of Language & Communication Disorders;2024-03-23

3. Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data;Aphasiology;2023-08-07