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患有亨廷顿舞蹈症的说话者的构音障碍亚组:两种数据驱动分类方法的比较

Dysarthria Subgroups in Talkers with Huntington's Disease: Comparison of Two Data-Driven Classification Approaches.

作者信息

Kim Daniel, Diehl Sarah, de Riesthal Michael, Tjaden Kris, Wilson Stephen M, Claassen Daniel O, Mefferd Antje S

机构信息

Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA.

Department of Communicative Disorders and Sciences, University at Buffalo, Buffalo, NY 14260, USA.

出版信息

Brain Sci. 2022 Apr 13;12(4):492. doi: 10.3390/brainsci12040492.

Abstract

Although researchers have recognized the need to better account for the heterogeneous perceptual speech characteristics among talkers with the same disease, guidance on how to best establish such dysarthria subgroups is currently lacking. Therefore, we compared subgroup decisions of two data-driven approaches based on a cohort of talkers with Huntington's disease (HD): (1) a statistical clustering approach (STATCLUSTER) based on perceptual speech characteristic profiles and (2) an auditory free classification approach (FREECLASS) based on listeners' similarity judgments. We determined the amount of overlap across the two subgrouping decisions and the perceptual speech characteristics driving the subgrouping decisions of each approach. The same speech samples produced by 48 talkers with HD were used for both grouping approaches. The STATCLUSTER approach had been conducted previously. The FREECLASS approach was conducted in the present study. Both approaches yielded four dysarthria subgroups, which overlapped between 50% to 78%. In both grouping approaches, overall bizarreness and speech rate characteristics accounted for the grouping decisions. In addition, voice abnormalities contributed to the grouping decisions in the FREECLASS approach. These findings suggest that apart from overall bizarreness ratings, indexing dysarthria severity, speech rate and voice characteristics may be important features to establish dysarthria subgroups in HD.

摘要

尽管研究人员已经认识到需要更好地考虑患有相同疾病的说话者之间异质的感知语音特征,但目前缺乏关于如何最好地建立此类构音障碍亚组的指导。因此,我们基于一组患有亨廷顿舞蹈症(HD)的说话者,比较了两种数据驱动方法的亚组决策:(1)一种基于感知语音特征概况的统计聚类方法(STATCLUSTER),以及(2)一种基于听众相似性判断的听觉自由分类方法(FREECLASS)。我们确定了两种亚组决策之间的重叠程度,以及驱动每种方法亚组决策的感知语音特征。两种分组方法均使用了48名患有HD的说话者产生的相同语音样本。STATCLUSTER方法先前已经进行过。FREECLASS方法是在本研究中进行的。两种方法均产生了四个构音障碍亚组,其重叠率在50%至78%之间。在两种分组方法中,总体怪异度和语速特征决定了分组。此外,语音异常在FREECLASS方法的分组决策中起作用。这些发现表明,除了总体怪异度评分外,表征构音障碍严重程度、语速和语音特征可能是在HD中建立构音障碍亚组的重要特征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5da8/9025673/743d9e2af6d4/brainsci-12-00492-g001.jpg

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