Publications

High performance computing (HPC) uses supercomputers and computing clusters to solve large computational problems. Frequently HPC resources are shared systems and access to restricted data sets or resources must be authenticated. These authentication needs can take multiple forms, both internal and external to the HPC cluster. A computational stack that...

Large-scale 3D scene reconstruction using Structure from Motion (SfM) continues to be very computationally challenging despite much active research in the area. We propose an efficient, scalable processing chain designed for cluster computing and suitable for use on aerial video. The sparse bundle adjustment step, which is iterative and difficult...

Graph analysis is used in many domains, from the social sciences to physics and engineering. The computational driver for one important class of graph analysis algorithms is the computation of leading eigenvectors of matrix representations of a graph. This paper explores the computational implications of performing an eigen decomposition of...

Big Data (as embodied by Hadoop clusters) and Big Compute (as embodied by MPI clusters) provide unique capabilities for storing and processing large volumes of data. Hadoop clusters make distributed computing readily accessible to the Java community and MPI clusters provide high parallel efficiency for compute intensive workloads. Bringing the...

In this paper, we demonstrate how informative dialect recognition systems such as acoustic pronunciation model (APM) help speech scientists locate and analyze phonetic rules efficiently. In particular, we analyze dialect-specific characteristics automatically learned from APM across two American English dialects. We show that unsupervised rule retrieval performs similarly to supervised...

We describe methods for constructing and using content graphs for query-by-example speaker recognition tasks within a large speech corpus. This goal is achieved as follows: First, we describe an algorithm for constructing speaker content graphs, where nodes represent speech signals and edges represent speaker similarity. Speech signal similarity can be...

In this paper, we extend our previous analysis of Gaussian Mixture Model (GMM) subspace compensation techniques using Gaussian modeling in the supervector space combined with additive channel and observation noise. We show that under the modeling assumptions of a total-variability i-vector system, full Gaussian supervector scoring can also be performed...

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...

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...

Though significant progress has been made in recent years, traditional work in social networks has focused on static network analysis or dynamics in a large-scale sense. In this work, we explore ways in which temporal information from sociographic data can be used for the analysis and prediction of individual and...