Neurophysiological Vocal Source Modeling for Biomarkers of Disease

Speech is potentially a rich source of biomarkers for detecting and monitoring neuropsychological disorders. Current biomarkers typically comprise acoustic descriptors extracted from behavioral measures of source, filter, prosodic and linguistic cues. In contrast, in this paper, we extract vocal features based on a neurocomputational model of speech production, reflecting latent or internal motor control parameters that may be more sensitive to individual variation under neuropsychological disease. These features, which are constrained by neurophysiology, may be resilient to artifacts and provide an articulatory complement to acoustic features. Our features represent a mapping from a low-dimensional acoustics-based feature space to a high-dimensional space that captures the underlying neural process including articulatory commands and auditory and somatosensory feedback errors. In particular, we demonstrate a neurophysiological vocal source model that generates biomarkers of disease by modeling vocal source control. By using the fundamental frequency contour and a biophysical representation of the vocal source, we infer two neuromuscular time series whose coordination provides vocal features that are applied to depression and Parkinson’s disease as examples. These vocal source coordination features alone, on a single held vowel, outperform or are comparable to other features sets and reflect a significant compression of the feature space.United States. Air Force (Contract No. FA8721-05-C-0002)United States. Air Force (Contract No. FA8702-15- D-0001

Long Term Suboxone™ Emotional Reactivity As Measured by Automatic Detection in Speech

Addictions to illicit drugs are among the nation’s most critical public health and societal problems. The current opioid prescription epidemic and the need for buprenorphine/naloxone (Suboxone®; SUBX) as an opioid maintenance substance, and its growing street diversion provided impetus to determine affective states (“true ground emotionality”) in long-term SUBX patients. Toward the goal of effective monitoring, we utilized emotion-detection in speech as a measure of “true” emotionality in 36 SUBX patients compared to 44 individuals from the general population (GP) and 33 members of Alcoholics Anonymous (AA). Other less objective studies have investigated emotional reactivity of heroin, methadone and opioid abstinent patients. These studies indicate that current opioid users have abnormal emotional experience, characterized by heightened response to unpleasant stimuli and blunted response to pleasant stimuli. However, this is the first study to our knowledge to evaluate “true ground” emotionality in long-term buprenorphine/naloxone combination (Suboxone™). We found in long-term SUBX patients a significantly flat affect (p<0.01), and they had less self-awareness of being happy, sad, and anxious compared to both the GP and AA groups. We caution definitive interpretation of these seemingly important results until we compare the emotional reactivity of an opioid abstinent control using automatic detection in speech. These findings encourage continued research strategies in SUBX patients to target the specific brain regions responsible for relapse prevention of opioid addiction.United States. Dept. of Defense. Assistant Secretary of Defense for Research & Engineering (Air Force Contract FA8721-05-C-0002

Digital Signal Processing

Contains research objectives and summary of research on seven research projects.Joint Services Electronics Program (Contract DAAB07-76-C-1400)U. S. Navy - Office of Naval Research (Contract N00014-75-C-0951-NR 049-308)National Science Foundation (Grant ENG71-02319-AO2

Digital Signal Processing

Contains research objectives and summary of research on seven research projects.U. S. Navy Office of Naval Research (Contract N00014-75-C-0951)National Science Foundation (Grant ENG71-02319-A02

Digital Signal Processing

Contains reports on twelve research projects.U. S. Navy - Office of Naval Research (Contract N00014-75-C-0951)National Science Foundation (Grant ENG76-24117)National Aeronautics and Space Administration (Grant NSG-5157)Joint Services Electronics Program (Contract DAABO7-76-C-1400)U.S. Navy-Office of Naval Research (Contract N00014-77-C-0196)Woods Hole Oceanographic InstitutionU. S. Navy - Office of Naval Research (Contract N00014-75-C-0852)Department of Ocean Engineering, M.I.T.National Science Foundation subcontract to Grant GX 41962 to Woods Hole Oceanographic Institutio

Digital Signal Processing

Contains a research summary and reports on fifteen research projects.National Science Foundation FellowshipJoint Services Electronics Program (Contract DAAG29-78-C-0020)National Science Foundation (Grant ENG76-24117)U.S. Navy - Office of Naval Research (Contract N00014-75-C-0951)National Science Foundation (Grant ENG76-24117)Schlumberger-Doll Research Center FellowshipHertz Foundation FellowshipNational Aeronautics and Space Administration (Grant NSG-5157)U.S. Navy - Office of Naval Research (Contract N00014-77-C-0196

Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?

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