Hypokinetic dysarthria (HD) occurs in 90% of Parkinson's disease (PD) patients. It manifests specifically in the areas of articulation, phonation, prosody, speech fluency, and faciokinesis. We aimed to systematically review papers on HD in PD with a special focus on (1) early PD diagnosis and monitoring of the disease progression using acoustic voice and speech analysis, and (2) functional imaging studies exploring neural correlates of HD in PD, and (3) clinical studies using acoustic analysis to evaluate effects of dopaminergic medication and brain stimulation. A systematic literature search of articles written in English before March 2016 was conducted in the Web of Science, PubMed, SpringerLink, and IEEE Xplore databases using and combining specific relevant keywords. Articles were categorized into three groups: (1) articles focused on neural correlates of HD in PD using functional imaging (n = 13); (2) articles dealing with the acoustic analysis of HD in PD (n = 52); and (3) articles concerning specifically dopaminergic and brain stimulation-related effects as assessed by acoustic analysis (n = 31); the groups were then reviewed. We identified 14 combinations of speech tasks and acoustic features that can be recommended for use in describing the main features of HD in PD. While only a few acoustic parameters correlate with limb motor symptoms and can be partially relieved by dopaminergic medication, HD in PD seems to be mainly related to non-dopaminergic deficits and associated particularly with non-motor symptoms. Future studies should combine non-invasive brain stimulation with voice behavior approaches to achieve the best treatment effects by enhancing auditory-motor integration.

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Parkinson's disease (PD) is a neurodegenerative disorder which impairs motor skills, speech, and other functions such as behavior, mood, and cognitive processes. One of the most typical clinical hallmarks of PD is handwriting deterioration, usually the first manifestation of PD. The aim of this study is twofold: (a) to find a subset of handwriting features suitable for identifying subjects with PD and (b) to build a predictive model to efficiently diagnose PD. We collected handwriting samples from 37 medicated PD patients and 38 age- and sex-matched controls. The handwriting samples were collected during seven tasks such as writing a syllable, word, or sentence. Every sample was used to extract the handwriting measures. In addition to conventional kinematic and spatio-temporal handwriting measures, we also computed novel handwriting measures based on entropy, signal energy, and empirical mode decomposition of the handwriting signals. The selected features were fed to the support vector machine classifier with radial Gaussian kernel for automated diagnosis. The accuracy of the classification of PD was as high as 88.13%, with the highest values of sensitivity and specificity equal to 89.47% and 91.89%, respectively. Handwriting may be a valuable marker as a diagnostic and screening tool.

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The objective of this paper is to present the state of-the-art relating to automatic speech and voice analysis techniques as applied to the monitoring of patients suffering from Alzheimer's disease as well as to shed light on possible future research topics. This work reviews more than 90 papers in the existing literature and focuses on the main feature extraction techniques and classification methods used. In order to guide researchers interested in working in this area, the most frequently used data repositories are also given. Likewise, it identifies the most clinically relevant results and the current lines developed in the field. Automatic speech analysis, within the Health 4.0 framework, offers the possibility of assessing these patients, without the need for a specific infrastructure, by means of non-invasive, fast and inexpensive techniques as a complement to the current diagnostic methods.

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