Loquens, Vol 4, No 1 (2017)

Monitoring Parkinson Disease from speech articulation kinematics


https://doi.org/10.3989/loquens.2017.036

Pedro Gómez
Neuromorphic Processing Laboratory (NeuVox Lab). Center for Biomedical Technology, Universidad Politécnica de Madrid, Spain
orcid http://orcid.org/0000-0003-3283-378X

Jiri Mekyska
Department of Telecommunications, Brno University of Technology, Czech Republic
orcid http://orcid.org/0000-0002-6195-193X

Andrés Gómez
Neuromorphic Processing Laboratory (NeuVox Lab). Center for Biomedical Technology, Universidad Politécnica de Madrid, Spain
orcid http://orcid.org/0000-0001-8643-9871

Daniel Palacios
Neuromorphic Processing Laboratory (NeuVox Lab). Center for Biomedical Technology, Universidad Politécnica de Madrid, Spain
orcid http://orcid.org/0000-0001-6063-4898

Victoria Rodellar
Neuromorphic Processing Laboratory (NeuVox Lab) Center for Biomedical Technology, Universidad Politécnica de Madrid, Spain
orcid http://orcid.org/0000-0001-9384-3290

Agustín Álvarez
Neuromorphic Processing Laboratory (NeuVox Lab) Center for Biomedical Technology, Universidad Politécnica de Madrid, Spain
orcid http://orcid.org/0000-0002-3387-6709

Abstract


Parkinson Disease (PD) is a neuromotor illness affecting general movements of different muscles, those implied in speech production being among them. The relevance of speech in monitoring illness progression has been documented in these last two decades. Most of the studies have concentrated in dysarthria and dysphonia induced by the syndrome. The present work is devoted to explore how PD affects the dynamic behavior of the speech neuromotor biomechanics (neuromechanics) involved in deficient articulation (dysarthria), in contrast to classical measurements based on static features as extreme and central vowel triangle positions. A statistical distribution of the kinematic velocity of the lower jaw and tongue is introduced, which presents interesting properties regarding pattern recognition and classification. This function may be used to establish distances between different articulation profiles in terms of information theory. Results show that these distances are correlated with a set of tests currently used by neurologists in PD progress evaluation, and could be used in elaborating new speech testing protocols.

Keywords


neuromotor deterioration; Parkinson Disease speech; hypokinetic dysarthria; speech articulation modeling; speech neuromechanics

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