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Descripción
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| Neurodegenerative pathologies produce important distortions in speech. Parkinson?s Disease (PD) leaves marks in fluency, prosody, articulation and phonation. Certain measurements based in configurations of the articulation organs inferred from formant positions, as the Vocal Space Area (VSA) or the Formant Centralization Ratio (FCR) have been classically used in this sense, but these markers represent mainly the static positions of sustained vowels on the vowel triangle. The present study proposes a measurement based on the mutual information contents of kinematic correlates derived from formant dynamics. An absolute kinematic velocity associated to the position of the articulation organs, involving the jaw and tongue is estimated and modelled statistically. The distribution of this feature is rather different in PD patients than in normative speakers when sustained vowels are considered. Therefore, articulation failures may be detected even in single sustained vowels. The study has processed a limited database of 40 female and 54 male PD patients, contrasted to a very selected and stable set of normative speakers. Distances based on Kullback-Leibler?s Divergence have shown to be sensitive to PD articulation instability. Correlation measurements show that the distance proposed shows statistically relevant relationship with certain motor and non-motor behavioral observations, as freezing of gait, or sleep disorders. These results point out to the need of defining scoring scales specifically designed for speech-based diagnose and monitoring methodologies in degenerative diseases of neuromotor origin. | |
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Internacional
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Si |
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Nombre congreso
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International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017 |
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Tipo de participación
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960 |
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Lugar del congreso
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Coruña |
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Revisores
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Si |
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ISBN o ISSN
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0302-9743 |
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DOI
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10.1007/978-3-319-59773-7 3 |
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Fecha inicio congreso
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19/06/2017 |
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Fecha fin congreso
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23/06/2017 |
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Desde la página
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21 |
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Hasta la página
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31 |
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Título de las actas
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Natural and Artificial Computation for Biomedicine and Neuroscience |