Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F17%3APU123582" target="_blank" >RIV/00216305:26220/17:PU123582 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/8076086/" target="_blank" >http://ieeexplore.ieee.org/document/8076086/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP.2017.8076086" target="_blank" >10.1109/TSP.2017.8076086</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation

Popis výsledku v původním jazyce

Up to 90% of patients with Parkinson’s disease (PD) suffer from hypokinetic dysarthria (HD). In this work, we analysed the power of conventional speech features quantifying imprecise articulation, dysprosody, speech dysfluency and speech quality deterioration extracted from a specialized poem recitation task to discriminate dysarthric and healthy speech. For this purpose, 152 speakers (53 healthy speakers, 99 PD patients) were examined. Only mildly strong correlation between speech features and clinical status of the speakers was observed. In the case of univariate classification analysis, sensitivity of 62.63% (imprecise articulation), 61.62% (dysprosody), 71.72% (speech dysfluency) and 59.60% (speech quality deterioration) was achieved. Multivariate classification analysis improved the classification performance. Sensitivity of 83.42% using only two features describing imprecise articulation and speech quality deterioration in HD was achieved. We showed the promising potential of the selected speech features and especially the use of poem recitation task to quantify and identify HD in PD.

Název v anglickém jazyce

Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation

Popis výsledku anglicky

Up to 90% of patients with Parkinson’s disease (PD) suffer from hypokinetic dysarthria (HD). In this work, we analysed the power of conventional speech features quantifying imprecise articulation, dysprosody, speech dysfluency and speech quality deterioration extracted from a specialized poem recitation task to discriminate dysarthric and healthy speech. For this purpose, 152 speakers (53 healthy speakers, 99 PD patients) were examined. Only mildly strong correlation between speech features and clinical status of the speakers was observed. In the case of univariate classification analysis, sensitivity of 62.63% (imprecise articulation), 61.62% (dysprosody), 71.72% (speech dysfluency) and 59.60% (speech quality deterioration) was achieved. Multivariate classification analysis improved the classification performance. Sensitivity of 83.42% using only two features describing imprecise articulation and speech quality deterioration in HD was achieved. We showed the promising potential of the selected speech features and especially the use of poem recitation task to quantify and identify HD in PD.

Druh

D - Stať ve sborníku

CEP obor

—

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

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

40th Anniversary of International Conference on Telecommunications and Signal Processing (TSP)

ISBN

978-1-5090-3981-4

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

739-742

Název nakladatele

Neuveden

Místo vydání

Barcelona, Španělsko

Místo konání akce

Barcelona

Datum konání akce

5. 7. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000425229000157