Extraction of Voice Parameters from Continuous Running Speech for Pulmonary Disease Monitoring Pulmonary disease is one of the leading causes of death, and individuals with chronic diseases like asthma and chronic obstructive pulmonary disease (COPD) will have to manage their disease throughout their lifetime. Passive monitoring with mobile devices such as smartphones represents a costeffective solution that is closely coupled with the user to detect and continuously track adverse pulmonary trends. Identifying changes in the useru0027s voice patterns has seen recent research interest as a potentially useful biomarker that is relatively convenient and efficient to monitor. Prosodic features of the voice such as shimmer and jitter have been traditionally extracted from sustained vowel sounds in the majority of the previous work in this area. In this work, we describe a method to extract these parameters from continuous running speech, and show that they have better agreement with the corresponding parameters from sustained vowel sounds. This method was validated on real data from pulmonary patients collected under two different environments, and also compared to an existing speech processing package that is widely used in the literature to extract such features. The described method can be an important step in realizing passive monitoring of voice changes from the natural speech of pulmonary patients in their day to day lives.

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.