Publications
A vocal modulation model with application to predicting depression severity
Summary
Summary
Speech provides a potential simple and noninvasive "on-body" means to identify and monitor neurological diseases. Here we develop a model for a class of vocal biomarkers exploiting modulations in speech, focusing on Major Depressive Disorder (MDD) as an application area. Two model components contribute to the envelope of the speech...
Assessing functional neural connectivity as an indicator of cognitive performance
Summary
Summary
Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability of properties of neural connectivity...
Estimating lower vocal tract features with closed-open phase spectral analyses
Summary
Summary
Previous studies have shown that, in addition to being speaker-dependent yet context-independent, lower vocal tract acoustics significantly impact the speech spectrum at mid-to-high frequencies (e.g 3-6kHz). The present work automatically estimates spectral features that exhibit acoustic properties of the lower vocal tract. Specifically aiming to capture the cyclicity property of...
Speech enhancement using sparse convolutive non-negative matrix factorization with basis adaptation
Summary
Summary
We introduce a framework for speech enhancement based on convolutive non-negative matrix factorization that leverages available speech data to enhance arbitrary noisy utterances with no a priori knowledge of the speakers or noise types present. Previous approaches have shown the utility of a sparse reconstruction of the speech-only components of...
Vocal-source biomarkers for depression - a link to psychomotor activity
Summary
Summary
A hypothesis in characterizing human depression is that change in the brain's basal ganglia results in a decline of motor coordination. Such a neuro-physiological change may therefore affect laryngeal control and dynamics. Under this hypothesis, toward the goal of objective monitoring of depression severity, we investigate vocal-source biomarkers for depression...
Exploring the impact of advanced front-end processing on NIST speaker recognition microphone tasks
Summary
Summary
The NIST speaker recognition evaluation (SRE) featured microphone data in the 2005-2010 evaluations. The preprocessing and use of this data has typically been performed with telephone bandwidth and quantization. Although this approach is viable, it ignores the richer properties of the microphone data-multiple channels, high-rate sampling, linear encoding, ambient noise...
FY11 Line-Supported Bio-Next Program - Multi-modal Early Detection Interactive Classifier (MEDIC) for mild traumatic brain injury (mTBI) triage
Summary
Summary
The Multi-modal Early Detection Interactive Classifier (MEDIC) is a triage system designed to enable rapid assessment of mild traumatic brain injury (mTBI) when access to expert diagnosis is limited as in a battlefield setting. MEDIC is based on supervised classification that requires three fundamental components to function correctly; these are...
Investigating acoustic correlates of human vocal fold vibratory phase asymmetry through modeling and laryngeal high-speed videoendoscopy
Summary
Summary
Vocal fold vibratory asymmetry is often associated with inefficient sound production through its impact on source spectral tilt. This association is investigated in both a computational voice production model and a group of 47 human subjects. The model provides indirect control over the degree of left-right phase asymmetry within a...
Automatic detection of depression in speech using Gaussian mixture modeling with factor analysis
Summary
Summary
Of increasing importance in the civilian and military population is the recognition of Major Depressive Disorder at its earliest stages and intervention before the onset of severe symptoms. Toward the goal of more effective monitoring of depression severity, we investigate automatic classifiers of depression state, that have the important property...
Sinewave representations of nonmodality
Summary
Summary
Regions of nonmodal phonation, exhibiting deviations from uniform glottal-pulse periods and amplitudes, occur often and convey information about speaker- and linguistic-dependent factors. Such waveforms pose challenges for speech modeling, analysis/synthesis, and processing. In this paper, we investigate the representation of nonmodal pulse trains as a sum of harmonically-related sinewaves with...