Acoustic Analysis of Sentences Complicated for Articulation in Patients with Parkinson's Disease

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU118915" target="_blank" >RIV/00216305:26220/16:PU118915 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Acoustic Analysis of Sentences Complicated for Articulation in Patients with Parkinson's Disease

Popis výsledku v původním jazyce

This paper deals with acoustic analysis of hypokinetic dysarthria. Hypokinetic dysarthria is a speech motor dysfunction that is present in approximately 90% of patients with Parkinson’s disease (PD). The work is mainly focused on parameterization techniques that can be used to diagnose or monitor this disease as well as estimate its progress. Acoustic analysis can be used to estimate a grade of hypokinetic dysarthria in fields of phonation, articulation, prosody and speech fluency. Regarding the parameterization, new features based on RASTA method were proposed. The analysis is based on parametrization of sentences complicated for articulation. Experimental dataset consists of 101 PD patients with different disease progress and 53 healthy controls. For the purpose of feature selection we employed mRMR (minimum Redundancy Maximum Relevance) method.

Název v anglickém jazyce

Acoustic Analysis of Sentences Complicated for Articulation in Patients with Parkinson's Disease

Popis výsledku anglicky

This paper deals with acoustic analysis of hypokinetic dysarthria. Hypokinetic dysarthria is a speech motor dysfunction that is present in approximately 90% of patients with Parkinson’s disease (PD). The work is mainly focused on parameterization techniques that can be used to diagnose or monitor this disease as well as estimate its progress. Acoustic analysis can be used to estimate a grade of hypokinetic dysarthria in fields of phonation, articulation, prosody and speech fluency. Regarding the parameterization, new features based on RASTA method were proposed. The analysis is based on parametrization of sentences complicated for articulation. Experimental dataset consists of 101 PD patients with different disease progress and 53 healthy controls. For the purpose of feature selection we employed mRMR (minimum Redundancy Maximum Relevance) method.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů