Cognitive and Acoustic Speech and Language Patterns Occurring in Different Neurodegenerative Disorders while Performing Neuropsychological Tests

In the last decade, improvements in automated speech processing, powered by signal processing and machine learning, has led to new approaches for medical assessment. Additionally, previous research in clinical speech has identified interpretable measures that are sensitive to changes in the cognitive, linguistic, affective, and motoric domains. In order to include speech-based automatic approaches in clinical applications, factors such as robustness, specificity, and interpretability of speech features are crucial. We focused on the analysis of a multi-modal array of interpretable features obtained from the spoken responses of participants with Neurodegenerative Diseases (ND) and control participants (CN) to neuropsychological tests. ND participants have Alzheimer's disease (AD), Parkinson's disease (PD), or Parkinson's disease mimics (PDM). We first collected spoken responses to three tests, a modified version of the Stroop test (MST), a verb naming task (VNT), and a noun naming task (NNT). Then, we arranged two complementary sets of cognitive and acoustic features and analyzed their statistical significance between the groups studied. Our results suggested that AD participants had significantly greater reaction times and significantly lower response accuracy with respect to the other groups across tests. In addition, PDM participants, compared to CN and PD participants, took a significantly longer time to complete the MST and NNT, while all the groups of participants with NDs showed significantly lower confidence during the MST. Since the analyzed features provided good differentiation results, they can be used in diagnostic tools for the assessment of NDs.