Speech Acoustic Markers Can Detect Mild Cognitive Impairment in Parkinson's Disease.

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.