Validation of freelyavailable pitch detection algorithms across various noise levels in assessing speech captured by smartphone in Parkinsonxe2x80x99s disease Abstract   Measuring the fundamental frequency of the vocal folds F0 is recognized as an important parameter in the assessment of speech impairments in Parkinsons disease (PD). Although a number of F0 trackers currently exist, their performance in smartphonebased evaluation and robustness against background noise have never been tested. Monologues from 30 newlydiagnosed, untreated PD patients and 30 matched healthy control participants were collected. Additive nonstationary urban and household noise at different SNR levels was added to the recordings, which were subsequently assessed by 10 freelyavailable and widelyused pitchtracking algorithms. According to the comparison of all investigated pitch detectors, sawtooth inspired pitch estimator (SWIPE) was the most robust and accurate method in estimating mean F0 and its standard deviation. However, at a low 6xe2x80xafdB SNR level, a combination of more algorithms may be needed to achieve the desired precision. Monopitch, calculated as F0 standard deviation and estimated by SWIPE, proved to be robust in distinguishing between the PD and healthy control groups (p

Exploring TelephoneQuality Speech Signals Towards Parkinsons Disease Assessment in a Large Acoustically Noncontrolled Study The use of highquality speech signals has led to considerable breakthroughs in Parkinsonu0027s Disease (PD) in the last decade. These include accurate differentiation of PD versus Healthy Controls (HC) and monitoring longitudinal PD symptom severity. We recently concluded the Parkinsonu0027s Voice Initiative (PVI) study collecting data from a very large cohort under noncontrolled acoustic conditions. We acoustically characterized 11,942 recordings from 6531 USbased participants using 307 dysphonia measures. We selected a robust subset of 30 dysphonia measures using GramSchmidt Orthogonalization (GSO). We projected the data onto a twodimensional representation using tdistributed stochastic neighbor embedding to facilitate visual exploration, and used hierarchical clustering to understand data homogeneity. We demonstrate that there is considerable overlap in the projected feature space between PD and HC, making the binary classification task particularly challenging. The data was grouped into nine clusters using hierarchical clustering which was in broad agreement with the projected twodimensional representation. These results provide some new insights into understanding the new challenges posed in the PVI project where acoustic recordings conditions were not controlled.