A Multi-Modal Array of Interpretable Features to Evaluate Language and Speech Patterns in Different Neurological Disorders

Speech-based automatic approaches for evaluating neurological disorders (NDs) depend on feature extraction before the classification pipeline. It is preferable for these features to be interpretable to facilitate their development as diagnostic tools. This study focuses on the analysis of interpretable features obtained from the spoken responses of 88 subjects with NDs and controls (CN). Subjects with NDs have Alzheimer's disease (AD), Parkinson's disease (PD), or Parkinson's disease mimics (PDM). We configured three complementary sets of features related to cognition, speech, and language, and conducted a statistical analysis to examine which features differed between NDs and CN. Results suggested that features capturing response informativeness, reaction times, vocabulary richness, and syntactic complexity provided separability between AD and CN. Similarly, fundamental frequency variability helped differentiate PD from CN, while the number of salient informational units PDM from CN.