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.

LSTM Siamese Network for Parkinsonxe2x80x99s Disease Detection from Speech Parkinsonxe2x80x99s disease (PD) is a neurodegenerative disorder that severely affects motor functions. Symptoms include dysarthria and this fact has been the basis for PD detection from speech in several works. Machine learningbased technologies have made significant strides in automatic speech recognition, but their use is fairly limited for clinical diagnosis due to the lack of large corpora containing parkinsonian speech.In this work, we propose a twostep strategy to use machine learning methods for PD detection. In the first step, we use Long ShortTerm Memory (LSTM)based siamese networks to learn feature representations that highlight the information related to speech articulation and prosody relevant for PD detection. Siamese networks are trained on data pairs employing a Spanish corpus containing 52 patients and 56 control subjects. In the second step, we train a classifier to make decisions about the presence or absence of PD employing the features provided by the LSTM networks.We achieve an EER of 1.9% in the detection by combining the scores of different textdependent models. Preliminary experiments show the efficacy of the proposed method and prove the usefulness of LSTM for PD detection from speech.