Author: Aaron Lawson

In this work, we extend the TBC method, proposing a new similarity metric for selecting training data that results in significant gains over the one proposed in the original work.

This article focuses on speaker recognition using speech acquired using a single distant or far-field microphone in an indoors environment.

In this study, our aim is analyzing the behavior of the speaker recognition systems based on speaker embeddings toward different front-end features, including the standard MFCC, as well as PNCC, and PLP.

This work is a multi-organizational effort led by SRI International and Lab41 with the intent to push forward state-of-the-art distant microphone approaches in signal processing and speech recognition.

In this paper we analyze articulatory measures to gain further insight into how articulation is affected by depression.

In this paper, we focus on situations in which either (1) the system-modeled languages are not observed during use or (2) the test data contains OOS languages that are unseen during modeling or calibration. 

We benchmark these approaches on several distinctly different databases, after we describe our SRICON-UAM team system submission for the NIST 2016 SRE. 

This article is concerned with the issue of calibration in the context of Deep Neural Network (DNN) based approaches to speaker recognition. We propose a hybrid alignment framework, which stems from our previous work in DNN senone alignment, that uses the bottleneck features only for the alignment of features during statistics calculation.

This article provides details of the SITW speaker recognition challenge and analysis of evaluation results. We provide an analysis of some of the top performing systems submitted during the evaluation and provide future research directions.

The Speakers in the Wild (SITW) speaker recognition database contains hand-annotated speech samples from open-source media for the purpose of benchmarking text-independent speaker recognition technology.

Using bottleneck features extracted from a deep neural network (DNN) trained to predict senone posteriors has resulted in new, state-of-the-art technology for language and speaker identification.

We introduce a new dataset for the study of the effect of highly non-stationary noises on language recognition (LR) performance.