Toward Automated Articulation Rate Analysis via Connected Speech in Dysarthrias
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00356780" target="_blank" >RIV/68407700:21230/22:00356780 - isvavai.cz</a>
Alternative codes found
RIV/00064165:_____/22:10445711 RIV/00216208:11110/22:10445711
Result on the web
<a href="https://doi.org/10.1044/2021_JSLHR-21-00549" target="_blank" >https://doi.org/10.1044/2021_JSLHR-21-00549</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1044/2021_JSLHR-21-00549" target="_blank" >10.1044/2021_JSLHR-21-00549</a>
Alternative languages
Result language
angličtina
Original language name
Toward Automated Articulation Rate Analysis via Connected Speech in Dysarthrias
Original language description
Purpose: This study aimed to evaluate the reliability of different approaches for estimating the articulation rates in connected speech of Parkinsonian patients with different stages of neurodegeneration compared to healthy controls. Method: Monologues and reading passages were obtained from 25 patients with idiopathic rapid eye movement sleep behavior disorder (iRBD), 25 de novo patients with Parkinson's disease (PD), 20 patients with multiple system atrophy (MSA), and 20 healthy controls. The recordings were subsequently evaluated using eight syllable localization algorithms, and their performances were compared to a manual transcript used as a reference. Results: The Google & Pyphen method, based on automatic speech recognition followed by hyphenation, outperformed the other approaches (automated vs. hand transcription: r > .87 for monologues and r > .91 for reading passages, p < .001) in precise feature estimates and resilience to dysarthric speech. The Praat script algorithm achieved sufficient robustness (automated vs. hand transcription: r > .65 for monologues and r > .78 for reading passages, p < .001). Compared to the control group, we detected a slow rate in patients with MSA and a tendency toward a slower rate in patients with iRBD, whereas the articulation rate was unchanged in patients with early untreated PD. Conclusions: The state-of-the-art speech recognition tool provided the most precise articulation rate estimates. If speech recognizer is not accessible, the freely available Praat script based on simple intensity thresholding might still provide robust properties even in severe dysarthria. Automated articulation rate assessment may serve as a natural, inexpensive biomarker for monitoring disease severity and a differential diagnosis of Parkinsonism
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
60203 - Linguistics
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Speech Language and Hearing Research
ISSN
1092-4388
e-ISSN
1558-9102
Volume of the periodical
65
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
1386-1401
UT code for WoS article
000830953900010
EID of the result in the Scopus database
2-s2.0-85128159932