A forced gaussians based methodology for the differential evaluation of Parkinson's Disease by means of speech processing
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00325946" target="_blank" >RIV/68407700:21230/19:00325946 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.bspc.2018.10.020" target="_blank" >https://doi.org/10.1016/j.bspc.2018.10.020</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.bspc.2018.10.020" target="_blank" >10.1016/j.bspc.2018.10.020</a>
Alternative languages
Result language
angličtina
Original language name
A forced gaussians based methodology for the differential evaluation of Parkinson's Disease by means of speech processing
Original language description
Literature evidences the existence of hypokinetic dysarthria in parkinsonian patients and, consequently, the objective characterization of the dysarthric signs associated to the articulatory aspect of speech can be used to detect Parkinson's Disease (PD) providing clinicians with new tools to support the clinical diagnosis. However, no work has analyzed in detail the importance of the different phonemes in the automatic detection of PD from the speech. This work proposes new approaches for this detection by using new classification schemes that allow to compare independently the different phonetic units of patients and controls employed during several speech tasks.Three different parkinsonian corpora were used allowing cross-validation and cross-corpora trials. The results of cross-validation trials (k-folds) provided accuracies between 81% and 94%, with AUC between 0.87 and 0.97 depending on the corpus, while cross-corpora trials yielded accuracies between 66% and 76% with AUC between 0.76 and 0.87. These results suggest that PD affects to the articulatory sequence as a whole, influencing more clearly phonetic units requiring a higher narrowing of the vocal tract. Additionally, text-dependent utterances are considered as the recommended speech task for the detection of PD in this type of schemes as these allow to compare more precisely the phonetic units of patients and controls. Lastly, this work discusses the existence of a glass ceiling in the accuracy of the systems for the automatic detection of PD using speech, concluding that this is below 95% for most of the cases.
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
20601 - Medical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Biomedical Signal Processing and Control
ISSN
1746-8094
e-ISSN
1746-8108
Volume of the periodical
48
Issue of the periodical within the volume
2
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
16
Pages from-to
205-220
UT code for WoS article
000452934400020
EID of the result in the Scopus database
2-s2.0-85055626594