Segment-dependent dynamics in predicting parkinson's disease

Early, accurate detection of Parkinson’s disease may aid in possible intervention and rehabilitation. Thus, simple noninvasive biomarkers are desired for determining severity. In this study, a novel set of acoustic speech biomarkers are introduced and fused with conventional features for predicting clinical assessment of Parkinson’s disease. We introduce acoustic biomarkers reflecting the segment dependence of changes in speech production components, motivated by disturbances in underlying neural motor, articulatory, and prosodic brain centers of speech. Such changes occur at phonetic and larger time scales, including multi-scale perturbations in formant frequency and pitch trajectories, in phoneme durations and their frequency of occurrence, and in temporal waveform structure. We also introduce articulatory features based on a neural computational model of speech production, the Directions into Velocities of Articulators (DIVA) model. The database used is from the Interspeech 2015 Computational Paralinguistic Challenge. By fusing conventional and novel speech features, we obtain Spearman correlations between predicted scores and clinical assessments of r = 0.63 on the training set (four-fold cross validation), r = 0.70 on a held-out development set, and r = 0.97 on a heldout test set.