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Title: Sparse predictive models for the analysis and classification of pathological speech
Duration: from 01/11/2021 to 31/12/2022 (could be extended to an advanced position)
Required Knowledge and background: A solid knowledge in speech/signal processing; A good mathematical background; Basics of machine learning; Programming in Matlab and Python.
Application and more information : https://jobs.inria.fr/public/classic/en/offres/2021-03570
Context and objectives : During this century, there has been an ever increasing interest in the development of objective vocal biomarkers to assist in diagnosis and monitoring of neurodegenerative diseases and, recently, respiratory diseases because of the Covid-19 pandemic. The literature is now relatively rich in methods for objective analysis of dysarthria, a class of motor speech disorders , where most of the effort has been made on speech impaired by Parkinson’s disease. However, relatively few studies have addressed the challenging problem of discrimination between subgroups of Parkinsonian disorders which share similar clinical symptoms, particularly is early disease stages . As for the analysis of speech impaired by respiratory diseases, the field is relatively new (with existing developments in very specialized areas) but is taking a great attention since the beginning of the pandemic.
On the other hand, the large majority of existing processing methods (of pathological speech in general) still heavily rely on a core of feature estimators designed and optimized for healthy speech. There exist thus a strong need for a framework to infer/design speech features and cues which remain robust to the perturbations caused by (classes of) disordered speech. The first and main objective of this proposal is to explore the framework of sparse modeling of speech which allow a certain flexibility in the design and parameter estimation of the source-filter model of speech production. This exploration will be essentially based on theoretical advances developed by the GEOSTAT team and which have led to a significant impact in the field of image processing, not only at the scientific level  but also at the technological level (www.inria.fr/fr/i2s-geostat-un-innovation-lab-en-imagerie-numerique).
The second objective of this proposal is to use the resulting representations as inputs to basic machine learning algorithms in order to conceive a vocal biomarker to assist in the discrimination between subgroups of Parkinsonian disorders (Parkinson’s disease, Multiple-System Atrophy, Progressive Supranuclear Palsy) and in the monitoring of respiratory diseases (Covid-19, Asthma, COPD).
Both objectives benefit from a rich dataset of speech and other biosignals recently collected in the framework of two clinical studies in partnership with university hospitals in Bordeaux and Toulouse (for Parkinsonian disorders) and in Paris (for respiratory diseases).
 J. Duffy. Motor Speech Disorders Substrates, Differential Diagnosis, and Management. Elsevier, 2013.
 J. Rusz et al. Speech disorders reflect differing pathophysiology in Parkinson's disease, progressive supranuclear palsy and multiple system atrophy. Journal of Neurology, 262(4), 2015.
 H. Badri. Sparse and Scale-Invariant Methods in Image Processing. PhD thesis, University of Bordeaux, France, 2015.