ArticleBrain sciences2022
Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Differentiating upper- and lower motor neuron diseases using automated acoustic analysis.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2025Article
- Neurolinguistic and acoustic analysis of articulatory impairments in Arabic speech disorders.Frontiers in human neuroscience · 2025Article
- Ataxic speech disorders and Parkinson's disease diagnostics via stochastic embedding of empirical mode decomposition.PloS one · 2023Article
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3 authors.
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Abstract
For the clinical assessment of motor speech disorders (MSDs) in French, the MonPaGe-2.0.s protocol has been shown to be sensitive enough to diagnose mild MSD based on a combination of acoustic and perceptive scores. Here, we go a step further by investigating whether these scores-which capture deviance on intelligibility, articulation, voice, speech rate, maximum phonation time, prosody, diadochokinetic rate-contribute to the differential diagnosis of MSDs. To this aim, we trained decision trees for two-class automatic classification of different pairs of MSD subtypes based on seven deviance scores that are computed in MonPaGe-2.0.s against matched normative data. We included 60 speakers with mild to moderate MSD from six neuropathologies (amyotrophic lateral sclerosis, Wilson, Parkinson and Kennedy disease, spinocerebellar ataxia, post-stroke apraxia of speech). The two-class classifications relied mainly on deviance scores from four speech dimensions and predicted with over 85% accuracy the patient's correct clinical category for ataxic, hypokinetic and flaccid dysarthria; classification of the other groups (apraxia of speech and mixed dysarthria) was slightly lower (79% to 82%). Although not perfect and only tested on small cohorts so far, the classification with deviance scores based on clinically informed features seems promising for MSD assessment and classification.
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