Open AccessValidating Automatic Diadochokinesis Analysis Methods Across Dysarthria Severity and Syllable Task in Amyotrophic Lateral Sclerosis
Author(s) -
Chelsea Tanchip,
Diego L. Guarín,
Scotia McKinlay,
Carolina Barnett,
Sanjay Kalra,
Angela Genge,
Lawrence Korngut,
Jordan R. Green,
James Berry,
Lorne Zinman,
Azadeh Yadollahi,
Agessandro Abrahão,
Yana Yunusova
Publication year - 2022
Publication title -
journal of speech, language, and hearing research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 138
eISSN - 1558-9102
pISSN - 1092-4388
DOI - 10.1044/2021_jslhr-21-00503
Subject(s) - dysarthria , speech recognition , syllable , computer science , amyotrophic lateral sclerosis , audiology , medicine , disease , pathology
Oral diadochokinesis (DDK) is a standard dysarthria assessment task. To extract automatic and semi-automatic DDK measurements, numerous DDK analysis algorithms based on acoustic signal processing are available, including amplitude based, spectral based, and hybrid. However, these algorithms have been predominantly validated in individuals with no perceptible to mild dysarthria. The behavior of these algorithms across dysarthria severity is largely unknown. Likewise, these algorithms have not been tested equally for various syllable types. The goal of this study was to evaluate the performance of five common DDK algorithms as a function of dysarthria severity, considering syllable types.