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Theses

Automated objective speech markers for differential diagnosis between parkinson's disease and atypical parkinsonian disorders

Abstract : Speech disorder is an early and prominent manifestation of neurological disorders. Therefore, the breakdown of speech disorders and detecting underlying pathophysiology have invaluable importance to clinical practice. Speech disorder is commonly attributed to aging onset, however, the pattern is mostly distinct for neurogenic voice. Parkinsonism is one of the neurological disorder that refers to idiopathic Parkinson’s Disease (PD) and Atypical Parkinsonian Syndromes (APS), such as Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA). Differential diagnosis of latter disease groups is remains an challenging task due to similar symptoms at the early stages, while early diagnostic certainty is essential for the patient because of the diverging prognosis. Indeed, despite recent efforts, no validated objective speech marker is currently available to guide the clinician for the differential diagnosis. This thesis thus aims to design and define the speech markers that would provide deep insight into speech disorders caused by neurological diseases, and followed by differential diagnosis.Analysis of speech disorder demands at least a speech database by which pattern of speech abnormalities can be assessed. Speech database consisting PD and MSA-P neurological diseases is not available in French language. Thus development of a speech database (Voice4PD-MSA) from PD and MSA-P groups was one of the target of this thesis. While developing Voice4PD-MSA database, we explored CzechData database comprises of speech samples in Czech language for differential diagnosis.The automatic algorithm always in demand to quantify perceptual and visual observation to capture particular speech disorders. Clinically interpretable speech components are considered to capture speech abnormalities in respiration, vowel production, articulator movements, and prosody by objective methods from sustained vowel, word-initial consonants, diadochokinetic (DDK) tasks, and continuous speech. Imprecise vowel comprises deficits in vocal folds opening and closing, involuntary movements of articulator, hypernasality, tremor, and changes in vowel space area are observed to be important for differential diagnosis of MSA-P and PD patients. In imprecise obstruents, devoicing in voiced obstruents and burst in fricatives (anti-spirantization) are identified as distinctive speech markers for MSA-P. In addition, speech indexes related to the subsystem of speech production and dysarthria yield encouraging differentiation and disease specificity in disease groups. Given small amount of data, two-dimensional speech features are designed such that one of the disease group predominates in one speech dimension and consequently discriminate disease groups with good classification score.Early differential diagnosis was another critical objective of the current investigation. The present study observed some encouraging indications about early differential diagnosis exploring the trend of speech markers w.r.t. clinical signs. Thus we aspire that the presented methodology in this thesis would serve as a potential diagnosis tool in clinical practice and further inspire to develop automatic methods to investigate speech disorders in parkinsonism.
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Submitted on : Friday, November 19, 2021 - 12:17:14 PM
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Biswajit Das. Automated objective speech markers for differential diagnosis between parkinson's disease and atypical parkinsonian disorders. Modeling and Simulation. Université de Bordeaux, 2021. English. ⟨NNT : 2021BORD0225⟩. ⟨tel-03436409⟩

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