Publications

We present RECOG (Recognition and Exploration of COntent Graphs), a system for visualizing and interacting with speaker content graphs constructed from large data sets of speech recordings. In a speaker content graph, nodes represent speech signals and edges represent speaker similarity. First, we describe a layout algorithm that optimizes content...

Social network analysis has undergone a renaissance with the ubiquity and quantity of content from social media, web pages, and sensors. This content is a rich data source for constructing and analyzing social networks, but its enormity and unstructured nature also present multiple challenges. Work at Lincoln Laboratory is addressing...

This chapter presents applications of graph embedding to the problem of text-independent speaker recognition. Speaker recognition is a general term encompassing multiple applications. At the core is the problem of speaker comparison-given two speech recordings (utterances), produce a score which measures speaker similarity. Using speaker comparison, other applications can be...

In this paper we seek to characterize traffic in the U.S. air transportation system, and to subsequently develop improved models of traffic demand. We model the air traffic within the U.S. national airspace system as dynamic weighted network. We employ techniques advanced by work in complex networks over the past...

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...

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...

This letter proposes a new model for preferential attachment in dynamic directed networks. This model consists of a linear time-invariant system that uses past observations to predict future attachment rates, and an innovation noise process that induces growth on vertices that previously had no attachments. Analyzing a large citation network...

As abstract representations of relational data, graphs and networks find wide use in a variety of fields, particularly when working in non- Euclidean spaces. Yet for graphs to be truly useful in in the context of signal processing, one ultimately must have access to flexible and tractable statistical models. One...

The problem of detecting a small, anomalous subgraph within a large background network is important and applicable to many fields. The non-Euclidean nature of graph data, however, complicates the application of classical detection theory in this context. A recent statistical framework for anomalous subgraph detection uses spectral properties of a...

Recent advances in the field of speaker recognition have resulted in highly efficient speaker comparison algorithms. The advent of these algorithms allows for leveraging a background set, consisting a large numbers of unlabeled recordings, to improve recognition. In this work, a relational graph, where nodes represent utterances and links represent...