Parkinson's disease is known to cause mild to profound communication impairments depending on the stage of progression of the disease. There is a growing interest in home-based assessment tools for measuring severity of Parkinson's disease and speech is an appealing source of evidence. This paper reports tasks to elicit a versatile sample of voice production, algorithms to extract useful information from speech and models to predict the severity of the disease. Apart from standard features from time domain (e.g., energy, speaking rate), spectral domain (e.g., pitch, spectral entropy) and cepstral domain (e.g, mel-frequency warped cepstral coefficients), we also estimate harmonic-to-noise ratio, shimmer and jitter using our recently developed algorithms. In a preliminary study, we evaluate the proposed paradigm on data collected through 2 clinics from 82 subjects in 116 assessment sessions. Our results show that the information extracted from speech, elicited through 3 tasks, can predict the severity of the disease to within a mean absolute error of 5.7 with respect to the clinical assessment using the Unified Parkinson's Disease Rating Scale; the range of target motor sub-scale is 0 to 108. Our analysis shows that elicitation of speech through less constrained task provides useful information not captured in widely employed phonation task. While still preliminary, our results demonstrate that the proposed computational approach has promising realworld applications such as in home-based assessment or in telemonitoring of Parkinson's disease.