Detection of Persons with Parkinson's Disease by Acoustic, Vocal, and Prosodic Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00190035" target="_blank" >RIV/68407700:21230/11:00190035 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.asru2011.org/" target="_blank" >http://www.asru2011.org/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ASRU.2011.6163978" target="_blank" >10.1109/ASRU.2011.6163978</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection of Persons with Parkinson's Disease by Acoustic, Vocal, and Prosodic Analysis

Popis výsledku v původním jazyce

70% to 90% of patients with Parkinson's disease (PD) show an affected voice. Various studies revealed, that voice and prosody is one of the earliest indicators of PD. The issue of this study is to automatically detect whether the speech/voice of a personis affected by PD. We employ acoustic features, prosodic features and features derived from a two-mass model of the vocal folds on different kinds of speech tests: sustained phonations, syllable repetitions, read texts and monologues. Classification isperformed in either case by SVMs. A correlation-based feature selection was performed, in order to identify the most important features for each of these systems. We report recognition results of 91% when trying to differentiate between normal speaking persons and speakers with PD in early stages with prosodic modeling. With acoustic modeling we achieved a recognition rate of 88% and with vocal modeling we achieved 79%. After feature selection these results could reatly be improved. But

Název v anglickém jazyce

Detection of Persons with Parkinson's Disease by Acoustic, Vocal, and Prosodic Analysis

Popis výsledku anglicky

70% to 90% of patients with Parkinson's disease (PD) show an affected voice. Various studies revealed, that voice and prosody is one of the earliest indicators of PD. The issue of this study is to automatically detect whether the speech/voice of a personis affected by PD. We employ acoustic features, prosodic features and features derived from a two-mass model of the vocal folds on different kinds of speech tests: sustained phonations, syllable repetitions, read texts and monologues. Classification isperformed in either case by SVMs. A correlation-based feature selection was performed, in order to identify the most important features for each of these systems. We report recognition results of 91% when trying to differentiate between normal speaking persons and speakers with PD in early stages with prosodic modeling. With acoustic modeling we achieved a recognition rate of 88% and with vocal modeling we achieved 79%. After feature selection these results could reatly be improved. But

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Automatic Speech Recognition and Understanding Workshop 2011

ISBN

978-1-4673-0367-5

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

478-483

Název nakladatele

IEEE Signal Processing Society

Místo vydání

Piscataway

Místo konání akce

Hawaii

Datum konání akce

11. 12. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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