Fougeron Cécile, Kodrasi Ina, Laganaro Marina
Laboratoire de Phonétique et Phonologie, UMR7018 CNRS/Université Sorbonne-Nouvelle, 75005 Paris, France.
Signal Processing for Communication Group, Idiap Research Institute, 1920 Martigny, Switzerland.
Brain Sci. 2022 Oct 29;12(11):1471. doi: 10.3390/brainsci12111471.
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.
对于法语中运动性言语障碍（MSD）的临床评估，MonPaGe - 2.0.s方案已被证明足够敏感，能够基于声学和感知评分的组合来诊断轻度MSD。在此，我们更进一步，研究这些评分——这些评分反映了在可懂度、清晰度、嗓音、语速、最大发声时间、韵律、重复运动速率方面的偏差——是否有助于MSD的鉴别诊断。为此，我们基于在MonPaGe - 2.0.s中针对匹配的标准数据计算出的七个偏差评分，训练了决策树用于对不同MSD亚型对进行两类自动分类。我们纳入了60名患有轻度至中度MSD的患者，他们来自六种神经病理学疾病（肌萎缩侧索硬化症、威尔逊病、帕金森病和肯尼迪病、脊髓小脑共济失调、中风后言语失用症）。两类分类主要依赖于四个言语维度的偏差评分，对于共济失调性、运动减少性和弛缓性构音障碍，预测患者正确临床类别的准确率超过85%；其他组（言语失用症和混合性构音障碍）的分类准确率略低（79%至82%）。尽管目前并不完美且仅在小样本队列中进行了测试，但基于临床相关特征的偏差评分分类对于MSD评估和分类似乎很有前景。
