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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 AtrophyProject 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 ofthe population, the prevalence is likely to increase over the next decade.Multiple-System Atrophy (MSA) is a rare and sporadic neurodegenerative adult disorder, ofprogressive evolution and of unknown etiology. The MSA has a prevalence of 2 to 5/100 000 and hasno effective treatment. It usually starts in the 6th decade and there is a slight male predominance. Ittakes 3 years on average from the first signs of the disease for a patient to require a walking aid, 4-6years 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 betweenPD and MSA is a very difficult task because, at the early stage of the diseases, patients look alike aslong as signs, such as dysautonomia, are not more clearly installed for MSA patients. There is currentlyno 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 motordisability and the onset of cognitive impairment. Terminology commonly used to describe thesedisorders is dysarthria [1].Like PD patients, depending on areas of the brain that are damaged, people with AMS may also havespeech disorders: difficulties of articulation, staccato rhythm, squeaky or muted voice. Dysarthria inAMS 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 discriminationbetween PD and MSA. The ultimate goal is to develop a numerical dysarthria measure, based on theanalysis of the speech signal of the patients, which allows objective discrimination between PD andMSA and would thus complement the tools currently available to neurologists in the differentialdiagnosis 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 ishowever ruling over current speech technology. Thus, from the methodological point of view, the goalof this thesis is to investigate the framework of nonlinear and turbulent systems, which is better suitedto 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 nonlinearspeech analysis algorithms recently developed in the GeoStat team [3]. The goal being to extractrelevant speech features to design new dysarthria measures that enable accurate discrimination betweenPD and MSA voices. This will also require investigation of machine learning theory in order to developrobust 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'sCenter of Haut-Lévêque Hospital, to set up the experimental protocol and data collection. The latterwill 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 ofSpeech 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.pdfAdvisor: K. DaoudiDuration: 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.