ABSTRACT

Background

Large-shared databases and automated language analyses allow for the application of new data analysis techniques that can shed new light on the connected speech of people with aphasia (PWA).

Aims

To identify coherent clusters of PWA based on language output using unsupervised statistical algorithms and to identify features that are most strongly associated with those clusters.

Methods & Procedures

Clustering and classification methods were applied to language production data from 168 PWA. Language samples were from a standard discourse protocol tapping four genres: free speech personal narratives, picture descriptions, Cinderella storytelling, and procedural discourse.

Outcomes & Results

Seven distinct clusters of PWA were identified by the K-means algorithm. Using the random forest algorithm, a classification tree was proposed and validated, showing 91% agreement with the cluster assignments. This representative tree used only two variables to divide the data into distinct groups: total words from free speech tasks and total closed-class words from the Cinderella storytelling task.

Conclusion

Connected speech data can be used to distinguish PWA into coherent groups, providing insight into traditional aphasia classifications, factors that may guide discourse research and clinical work.

中文翻译：

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加强失语症的分类：使用连接语音的统计分析

摘要

背景

大型共享数据库和自动语言分析允许应用新的数据分析技术，这些技术可以为失语症患者 (PWA) 的相关语音提供新的思路。

宗旨

使用无监督统计算法根据语言输出识别 PWA 的连贯集群，并识别与这些集群关联最密切的特征。

方法与程序

聚类和分类方法应用于来自 168 个 PWA 的语言生产数据。语言样本来自标准话语协议，涉及四种类型：自由言论个人叙述、图片描述、灰姑娘讲故事和程序性话语。

结果与结果

K-means 算法确定了七个不同的 PWA 集群。使用随机森林算法，提出并验证了分类树，显示与聚类分配的一致性为 91%。该代表性树仅使用两个变量将数据分为不同的组：来自自由演讲任务的总词数和来自灰姑娘讲故事任务的封闭类总词数。

结论

连接的语音数据可用于将 PWA 区分为连贯的组，提供对传统失语症分类、可能指导话语研究和临床工作的因素的洞察。