Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/69746
Título: Identification of hypokinetic dysarthria using acoustic analysis of poem recitation
Autores/as: Mucha, Jan
Galaz, Zoltan
Mekyska, Jiri
Kiska, Tomas
Zvoncak, Vojtech
Smekal, Zdenek
Eliasova, Ilona
Mrackova, Martina
Kostalova, Milena
Rektorova, Irena
Faundez-Zanuy, Marcos
Alonso-Hernandez, Jesus B. 
Clasificación UNESCO: 3314 Tecnología médica
Palabras clave: Acoustic Analysis
Binary Classification
Hypokinetic Dysarthria
Parkinson’S Disease
Poem Recitation
Fecha de publicación: 2017
Publicación seriada: International Conference On Telecommunications And Signal Processing, Tsp 2017
Conferencia: 40th International Conference on Telecommunications and Signal Processing, TSP 2017 
Resumen: 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 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.
URI: http://hdl.handle.net/10553/69746
ISBN: 9781509039821
DOI: 10.1109/TSP.2017.8076086
Fuente: 2017 40th International Conference on Telecommunications and Signal Processing, TSP 2017,v. 2017-January, p. 739-742
Colección:Actas de congresos
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