Publications

In this work, we are examining correlations between vocal articulatory features, ocular smooth pursuit measures, and features from the fMRI BOLD response in regions of interest (ROI) time series in a high school athlete population susceptible to repeated head impact within a sports season. Initial results have indicated relationships between...

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...

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...

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...

In this paper we describe the most recent MIT Lincoln Laboratory language recognition system developed for the NIST 2015 Language Recognition Evaluation (LRE). The submission features a fusion of five core classifiers, with most systems developed in the context of an i-vector framework. The 2015 evaluation presented new paradigms. First...

Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities. The shared representations are applied to multimedia event detection (MED) and evaluated in comparison to unimodal counterparts, as well as other feature...

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...

This paper presents a description of the MIT Lincoln Laboratory (MITLL) language recognition system developed for the NIST 2011 Language Recognition Evaluation (LRE). The submitted system consisted of a fusion of four core classifiers, three based on spectral similarity and one based on tokenization. Additional system improvements were achieved following...

We present a new perspective on the subspace compensation techniques that currently dominate the field of speaker recognition using Gaussian Mixture Models (GMMs). Rather than the traditional factor analysis approach, we use Gaussian modeling in the sufficient statistic supervector space combined with Probabilistic Principal Component Analysis (PPCA) within-class and shared...

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...