Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s
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 devia...
| Үндсэн зохиолчид: | , , |
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| Формат: | Өгүүллэг |
| Хэл сонгох: | English |
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MDPI AG
2022-10-01
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| Цуврал: | Brain Sciences |
| Нөхцлүүд: | |
| Онлайн хандалт: | https://www.mdpi.com/2076-3425/12/11/1471 |
| _version_ | 1827647073208500224 |
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| author | Cécile Fougeron Ina Kodrasi Marina Laganaro |
| author_facet | Cécile Fougeron Ina Kodrasi Marina Laganaro |
| author_sort | Cécile Fougeron |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:14:07Z |
| format | Article |
| id | doaj.art-556e2bf71c794c03b449b2c24a508abc |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-09T19:14:07Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-556e2bf71c794c03b449b2c24a508abc2023-11-24T03:56:28ZengMDPI AGBrain Sciences2076-34252022-10-011211147110.3390/brainsci12111471Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.sCécile Fougeron0Ina Kodrasi1Marina Laganaro2Laboratoire de Phonétique et Phonologie, UMR7018 CNRS/Université Sorbonne-Nouvelle, 75005 Paris, FranceSignal Processing for Communication Group, Idiap Research Institute, 1920 Martigny, SwitzerlandFaculty of Psychology and Educational Science, University of Geneva, 1205 Geneva, SwitzerlandFor 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.https://www.mdpi.com/2076-3425/12/11/1471dysarthriaapraxia of speechautomatic classificationdecision tree |
| spellingShingle | Cécile Fougeron Ina Kodrasi Marina Laganaro Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s Brain Sciences dysarthria apraxia of speech automatic classification decision tree |
| title | Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s |
| title_full | Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s |
| title_fullStr | Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s |
| title_full_unstemmed | Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s |
| title_short | Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s |
| title_sort | differentiation of motor speech disorders through the seven deviance scores from monpage 2 0 s |
| topic | dysarthria apraxia of speech automatic classification decision tree |
| url | https://www.mdpi.com/2076-3425/12/11/1471 |
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