Publications

Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are inadequate for handling the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a rethinking of parallel architectures for graph problems. Our processor utilizes innovations that include a sparse matrix-based graph...

Google BigTable's scale-out design for distributed key-value storage inspired a generation of NoSQL databases. Recently the NewSQL paradigm emerged in response to analytic workloads that demand distributed computation local to data storage. Many such analytics take the form of graph algorithms, a trend that motivated the GraphBLAS initiative to standardize...

Julia is a new language for writing data analysis programs that are easy to implement and run at high performance. Similarly, the Dynamic Distributed Dimensional Data Model (D4M) aims to clarify data analysis operations while retaining strong performance. D4M accomplishes these goals through a composable, unified data model on associative...

For decades the High Performance Computing (HPC) community has used web content, workshops and embedded HPC scientists to enable practitioners to harness the power of parallel and distributed computing. The most successful approaches, face-to-face tutorials and embedded professionals, don't scale. To create scalable, flexible, educational experiences for practitioners in all...

The goal of this paper is to describe significant corpora available to support speaker recognition research and evaluation, along with details about the corpora collection and design. We describe the attributes of high-quality speaker recognition corpora. Considerations of the application, domain, and performance metrics are also discussed. Additionally, a literature...

Effective monitoring of bulbar disease progression in persons with amyotrophic lateral sclerosis (ALS) requires rapid, objective, automatic assessment of speech loss. The purpose of this work was to identify acoustic features that aid in predicting intelligibility loss and speaking rate decline in individuals with ALS. Features were derived from statistics...

The epilarynx plays an important role in speech production, carrying information about the individual speaker and manner of articulation. However, precise acoustic behavior of this lower vocal tract structure is difficult to establish. Focusing on acoustics observable in natural speech, recent spectral processing techniques isolate a unique resonance with characteristics...

Large unstructured audio data sets have become ubiquitous and present a challenge for organization and search. One logical approach for structuring data is to find common speakers and link occurrences across different recordings. Prior approaches to this problem have focused on basic methodology for the linking task. In this paper...

Recent work has shown large performance gains using denoising DNNs for speech processing tasks under challenging acoustic conditions. However, training these DNNs requires large amounts of parallel multichannel speech data which can be impractical or expensive to collect. The effective use of synthetic parallel data as an alternative has been...

Spoken language recognition requires a series of signal processing steps and learning algorithms to model distinguishing characteristics of different languages. In this paper, we present a sparse discriminative feature learning framework for language recognition. We use sparse coding, an unsupervised method, to compute efficient representations for spectral features from a...