Descripción
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In this study, several automatic detectors of Parkinson?s Disease (PD) based on phonatory aspects were analyzed employing two different corpora containing speech from speakers with PD. The features employed to characterize phonation were jitter, shimmer, noise measurements, complexity, modulation spectrum features and perceptual linear predictive coefficients. To differentiate between speakers with and without PD a gaussian mixture model classification scheme was used. Then, the approach providing the best results was combined with a scheme using articulatory information of the speech in order to assess the complementarity between phonatory and articulatory aspects in the automatic detection of PD. Cross-validation trials (k-folds) employing exclusively phonatory information provided accuracies between 64% and 71%, with AUC between 0.68 and 0.80 depending on the corpus. Results suggest that a combination of all the analyzed features with a PCA dimensionality reduction produce the best accuracy, AUC and sensibility. Also, results indicate that phonatory approaches tend to be less accurate in PD detection than other articulatory approaches proposed in previous studies. Finally, results suggest the complementarity between the studied articulatory and phonatory approaches is low | |
Internacional
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Si |
Nombre congreso
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MAVEBA |
Tipo de participación
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960 |
Lugar del congreso
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Florencia, Italia |
Revisores
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Si |
ISBN o ISSN
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978-88-6453-951-5 |
DOI
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Fecha inicio congreso
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17/12/2019 |
Fecha fin congreso
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19/12/2019 |
Desde la página
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33 |
Hasta la página
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36 |
Título de las actas
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MODELS AND ANALYSIS OF VOCAL EMISSIONS FOR BIOMEDICAL APPLICATIONS 11th INTERNATIONAL WORKSHOP |