Repeatability of Commonly Used Speech and Language Features for Clinical Applications.
Repeatability of Commonly Used Speech and Language Features for Clinical Applications.
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DOI：
10.1159/000511671
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发表时间：
2020-09-01
影响因子：
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通讯作者：
Berisha, Visar
中科院分区：
文献类型：
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作者：
Stegmann, Gabriela M;Hahn, Shira;Berisha, Visar
语言的变化有可能为各种神经系统疾病的诊断和进展提供重要信息。许多研究人员依靠开源的语音特征来开发算法来测量临床人群的语音变化，因为它们方便易用。然而，在神经系统疾病的背景下，开源特征的可重复性尚未得到研究。方法：我们使用了健康对照、肌萎缩侧索硬化症患者和疑似额颞叶痴呆患者的纵向样本，并在这3个数据集上分别评估了声学和语言特征的可重复性。结果：用类内相关性（ICC）和组内变异系数（WSCV）评价重复性。在3组任务中，ICC的中位数在0.02 ~ 0.55之间，WSCV的中位数在29 ~ 79%之间。结论：我们的研究结果表明，使用开源工具包提取语音特征的可重复性较低。研究人员在开发具有开源语音功能的数字健康模型时应谨慎行事。我们在在线补充材料中提供了逐特征重复性结果的详细摘要（ICC， WSCV，测量SE， WSCV的一致性限制和最小可检测变化），以便研究人员可以将可重复性信息纳入他们开发的模型中。
INTRODUCTION: Changes in speech have the potential to provide important information on the diagnosis and progression of various neurological diseases. Many researchers have relied on open-source speech features to develop algorithms for measuring speech changes in clinical populations as they are convenient and easy to use. However, the repeatability of open-source features in the context of neurological diseases has not been studied.METHODS: We used a longitudinal sample of healthy controls, individuals with amyotrophic lateral sclerosis, and individuals with suspected frontotemporal dementia, and we evaluated the repeatability of acoustic and language features separately on these 3 data sets.RESULTS: Repeatability was evaluated using intraclass correlation (ICC) and the within-subjects coefficient of variation (WSCV). In 3 sets of tasks, the median ICC were between 0.02 and 0.55, and the median WSCV were between 29 and 79%.CONCLUSION: Our results demonstrate that the repeatability of speech features extracted using open-source tool kits is low. Researchers should exercise caution when developing digital health models with open-source speech features. We provide a detailed summary of feature-by-feature repeatability results (ICC, WSCV, SE of measurement, limits of agreement for WSCV, and minimal detectable change) in the online supplementary material so that researchers may incorporate repeatability information into the models they develop.