IEEE journal of selected topics in signal processing · 2025
Speech Acoustic Markers Can Detect Mild Cognitive Impairment in Parkinson's Disease.
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Smith Kara M, Williamson James R, Quatieri Thomas F
Аннотация
BACKGROUND: Speech biomarkers have been used to assess motor dysfunction in people with Parkinson's disease (PD), but speech biomarkers for mild cognitive impairment in PD (PD-MCI) have not been well studied.
OBJECTIVE: To identify speech acoustic features associated with PD-MCI and evaluate the performance of a model to discriminate PD-MCI from participants with normal cognitive status (PD-NC).
METHODS: We analyzed speech samples from 42 participants with PD, diagnosed as either PD-MCI or PD-NC using the Movement disorders Society Task Force Tier II criteria as a gold-standard classification of MCI. A reading passage and a picture description task were analyzed for acoustic features, which were used to generate individual and then a final fused Gaussian mixture model (GMM) to discriminate PD-MCI and PD-NC participants.
RESULTS: The picture description task yielded a larger number of acoustic features that were highly associated with PD-MCI status compared to the reading task. Fusing the model outputs from the picture description task resulted in an AUC = 0.82 for discriminating PD-MCI from PD-NC participants. The acoustic features associated with PD-MCI stemmed from multiple speech subsystems.
CONCLUSION: PD-MCI has a distinct speech acoustic signature that may be harnessed to develop better tools to detect and monitor this complication.
OBJECTIVE: To identify speech acoustic features associated with PD-MCI and evaluate the performance of a model to discriminate PD-MCI from participants with normal cognitive status (PD-NC).
METHODS: We analyzed speech samples from 42 participants with PD, diagnosed as either PD-MCI or PD-NC using the Movement disorders Society Task Force Tier II criteria as a gold-standard classification of MCI. A reading passage and a picture description task were analyzed for acoustic features, which were used to generate individual and then a final fused Gaussian mixture model (GMM) to discriminate PD-MCI and PD-NC participants.
RESULTS: The picture description task yielded a larger number of acoustic features that were highly associated with PD-MCI status compared to the reading task. Fusing the model outputs from the picture description task resulted in an AUC = 0.82 for discriminating PD-MCI from PD-NC participants. The acoustic features associated with PD-MCI stemmed from multiple speech subsystems.
CONCLUSION: PD-MCI has a distinct speech acoustic signature that may be harnessed to develop better tools to detect and monitor this complication.