INRIA PhD fellowship on Nonlinear speech analysis for differential diagnosis between Parkinson’s disease and Multiple-System Atrophy

Proposal for an INRIA PhD fellowship (Cordi-S)
Title of the proposal: Nonlinear speech analysis for differential diagnosis between Parkinson’s disease and Multiple-System Atrophy
Project Team INRIA: GeoStat (http://geostat.bordeaux.inria.fr/)
Author of the proposal research subject: Khalid Daoudi (khalid.daoudi @ inria.fr)
Keywords: speech processing, nonlinear speech analysis, machine learning, voice pathology,
dysphonia, dysarthria, Multiple-System Atrophy, Parkinson’s disease.
Scientific context:
Parkinson’s disease (PD) is the most common neurodegenerative disorder after Alzheimer’s disease.
Prevalence is 1.5% of the population over age 65 and affects about 143,000 French. Given the aging of
the population, the prevalence is likely to increase over the next decade.
Multiple-System Atrophy (MSA) is a rare and sporadic neurodegenerative adult disorder, of
progressive evolution and of unknown etiology. The MSA has a prevalence of 2 to 5/100 000 and has
no effective treatment. It usually starts in the 6th decade and there is a slight male predominance. It
takes 3 years on average from the first signs of the disease for a patient to require a walking aid, 4-6
years to be in a wheelchair and about 8 years to be bedridden.
The PD and MSA require different treatment and support. However, the differential diagnosis between
PD and MSA is a very difficult task because, at the early stage of the diseases, patients look alike as
long as signs, such as dysautonomia, are not more clearly installed for MSA patients. There is currently
no valid clinical nor biological marker for clear distinction between the two diseases at an early stage.
Goal:
Voice and speech disorders in Parkinson’s disease is a clinical marker that coincides with a motor
disability and the onset of cognitive impairment. Terminology commonly used to describe these
disorders is dysarthria [1].
Like PD patients, depending on areas of the brain that are damaged, people with AMS may also have
speech disorders: difficulties of articulation, staccato rhythm, squeaky or muted voice. Dysarthria in
AMS is more severe and early in the sense that it requires more early rehabilitation compared to PD.
Since dysarthria is an early symptom of both diseases, the purpose of this thesis is to use dysarthria,
through digital processing of voice recordings of patients as a mean for objective discrimination
between PD and MSA. The ultimate goal is to develop a numerical dysarthria measure, based on the
analysis of the speech signal of the patients, which allows objective discrimination between PD and
MSA and would thus complement the tools currently available to neurologists in the differential
diagnosis of the two diseases.
Project:
Pathological voices, such as in PD and MSA, generally present high non-linearity and turbulence.
Nonlinear/turbulent phenomena are not naturally suited to linear signal processing. The latter is
however ruling over current speech technology. Thus, from the methodological point of view, the goal
of this thesis is to investigate the framework of nonlinear and turbulent systems, which is better suited
to analyzing the range of nonlinear and turbulent phenomena observed in pathological voices in general
[2], and in PD and MSA voices in particular. We will adopt an approach based on novel nonlinear
speech analysis algorithms recently developed in the GeoStat team [3]. The goal being to extract
relevant speech features to design new dysarthria measures that enable accurate discrimination between
PD and MSA voices. This will also require investigation of machine learning theory in order to develop
robust classifiers (to discriminate between PD and MSA voices) and to make correspondence
(regression) between speech measures and standard clinical rates.
The PhD candidate will actively participate, in coordination with neurologists from the Parkinson’s
Center of Haut-Lévêque Hospital, to set up the experimental protocol and data collection. The latter
will consist in recording patient’s voices using DIANA or EVA2 workstation (http://www.sqlab.fr/).
References:
[1] Auzou, P.; Rolland, V.; Pinto, S., Ozsancak C. (eds.). Les dysarthries. Editions Solal. 2007.
[2] Baghai-Ravary L. ; Beet S.W. Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of
Speech Disorders. Springer 2013.
[3] PhD thesis of Vahid Khanagha. GeoStat team, INRIA Bordeaux-Sud Ouest. January 2013.
http://geostat.bordeaux.inria.fr/images/vahid%20khanagha%204737.pdf
Advisor: K. Daoudi
Duration: 3 years (starting fall 2013)
Prerequisites: Good level in signal/speech processing is necessary, as well as Matlab and C/C++
programing. Knowledge in machine learning would be a strong advantage.


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