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GOMEZ-RODELLAR, A. PALACIOS-ALONSO, D. FERRÁNDEZ VICENTE, J.M. MEKYSKA, J. ALVAREZ MARQUINA, A. GOMEZ-VILDA, P.
Original Title
Evaluating Instability on Phonation in Parkinson's Disease and Aging Speech
Type
book chapter
Language
English
Original Abstract
Speech is controlled by axial neuromotor systems, highly sensible to certain neurodegenerative illnesses as Parkinson’s Disease (PD). Patients suffering PD present important alterations in speech, which manifest in phonation, articulation, prosody and fluency. Usually phonation and articulation alterations are estimated using different statistical frameworks and methods. The present study introduces a new paradigm based on Information Theory fundamentals to use common statistical tools to differentiate and score PD speech on phonation and articulation estimates. A study describing the performance of a methodology based on this common framework on a database including 16 PD patients, 16 age-paired healthy controls (HC) and 16 mid-age normative subjects (NS) is presented. The results point out to the clear separation between PD patients and HC subjects with respect to NS, but an unclear differentiation between PD and HC. The most important conclusion is that special effort is needed to establish differentiating features between PD, and organic laryngeal, from aging speech.
Keywords
Parkinson’s disease; phonation distortion; aging speech; speech neuromechanics
Authors
GOMEZ-RODELLAR, A.; PALACIOS-ALONSO, D.; FERRÁNDEZ VICENTE, J.M.; MEKYSKA, J.; ALVAREZ MARQUINA, A.; GOMEZ-VILDA, P.
Released
10. 5. 2019
ISBN
978-3-030-19650-9
Book
From Bioinspired Systems and Biomedical Applications to Machine Learning
Pages from
340
Pages to
351
Pages count
12
URL
https://doi.org/10.1007/978-3-030-19651-6_33
BibTex
@inbook{BUT156879, author="GOMEZ-RODELLAR, A. and PALACIOS-ALONSO, D. and FERRÁNDEZ VICENTE, J.M. and MEKYSKA, J. and ALVAREZ MARQUINA, A. and GOMEZ-VILDA, P.", title="Evaluating Instability on Phonation in Parkinson's Disease and Aging Speech", booktitle="From Bioinspired Systems and Biomedical Applications to Machine Learning", year="2019", pages="340--351", doi="10.1007/978-3-030-19651-6\{_}33", isbn="978-3-030-19650-9", url="https://doi.org/10.1007/978-3-030-19651-6_33" }