Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HC). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, we analyzed 2759 telephone-quality voice recordings from 1483 PD and 15321 recordings from 8300 HC participants. To account for variations in phonetic backgrounds, we acquired data from seven countries. We developed a statistical framework for analyzing voice, whereby we computed 307 dysphonia measures that quantify different properties of voice impairment, such as, breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor. We used feature selection algorithms to identify robust parsimonious feature subsets, which were used in combination with a Random Forests (RF) classifier to accurately distinguish PD from HC. The best 10-fold cross-validation performance was obtained using Gram-Schmidt Orthogonalization (GSO) and RF, leading to mean sensitivity of 64.90% (standard deviation, SD 2.90%) and mean specificity of 67.96% (SD 2.90%). This large-scale study is a step forward towards assessing the development of a reliable, cost-effective and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice.

Monitoring the sensory consequences of articulatory movements supports speaking. For example, delaying auditory feedback of a speaker's voice disrupts speech production. Also, there is evidence that this disruption may be decreased by immediate visual feedback, i.e., seeing one's own articulatory movements. It is, however, unknown whether delayed visual feedback affects speech production in fluent speakers. Here, the effects of delayed auditory and visual feedback on speech fluency (i.e., speech rate and errors), vocal control (i.e., intensity and pitch), and speech rhythm were investigated. Participants received delayed (by 200 ms) or immediate auditory feedback, while repeating sentences. Moreover, they received either no visual feedback, immediate visual feedback, or delayed visual feedback (by 200, 400, and 600 ms). Delayed auditory feedback affected fluency, vocal control, and rhythm. Immediate visual feedback had no effect on any of the speech measures when it was combined with delayed auditory feedback. Delayed visual feedback did, however, affect speech fluency when it was combined with delayed auditory feedback. In sum, the findings show that delayed auditory feedback disrupts fluency, vocal control, and rhythm and that delayed visual feedback can strengthen the disruptive effect of delayed auditory feedback on fluency.

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This paper introduces a model being developed for estimating child and adolescent formant frequency values from adult data.The model approximates adult male and female pharyngeal and oral cavity lengths, and scales these along the corresponding male or female growth curve. The second and third formant frequencies are estimated directly from these scaled vocal tract dimensions.

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