Repeatability of Commonly Used Speech and Language Features for Clinical Applications.

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. 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. 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%. 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.

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Copyright © 2020 by S. Karger AG, Basel.

Déclaration de conflit d'intérêts

Dr. Visar Berisha is an associate professor at Arizona State University. He is a co-founder of Aural Analytics. Dr. Julie Liss is a professor and associate dean at Arizona State University. She is a co-founder of Aural Analytics. Dr. Jeremy Shefner is the Kemper and Ethel Marley Professor and Chair of Neurology at the Barrow Neurological Institute. He is a scientific advisor to Aural Analytics.