Evidence map›Paper›PMID 36358397›Full record

ArticleBrain sciences2022

Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s.

Cécile Fougeron, Ina Kodrasi, Marina Laganaro

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Differentiating upper- and lower motor neuron diseases using automated acoustic analysis.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Cécile FougeronLaboratoire de Phonétique et Phonologie, UMR7018 CNRS/Université Sorbonne-Nouvelle, 75005 Paris, France.
Ina KodrasiSignal Processing for Communication Group, Idiap Research Institute, 1920 Martigny, Switzerland.ORCID 0000-0002-1747-1322
Marina LaganaroFaculty of Psychology and Educational Science, University of Geneva, 1205 Geneva, Switzerland.ORCID 0000-0002-4054-0939

Funding

Swiss National Science Foundation CRSII5_173711Swiss National Science Foundation CRSII5_202228
6 · The paper itself

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.

Indexed as

apraxia of speechautomatic classificationdecision treedysarthria

Identifiers

PMID36358397
PMCPMC9688649

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