Publications

This paper investigates the problem of speaker identification and verification in noisy conditions, assuming that speech signals are corrupted by environmental noise, but knowledge about the noise characteristics is not available. This research is motivated in part by the potential application of speaker recognition technologies on handheld devices or the...

A description of the MIT Lincoln Laboratory system used in the person identification task of the recent CLEAR 2007 Evaluation is documented in this paper. This task is broken into audio, visual, and multimodal subtasks. The audio identification system utilizes both a GMM and a SVM subsystem, while the visual...

Cross-channel degradation is one of the significant challenges facing speaker recognition systems. We study this problem in the support vector machine (SVM) context and nuisance variable compensation in high-dimensional spaces more generally. We present an approach to nuisance variable compensation by removing nuisance attribute-related dimensions in the SVM expansion space...

In this chapter, we focus on the area of text-independent speaker verification, with an emphasis on unconstrained telephone conversational speech. We begin by providing a general likelihood ratio detection task framework to describe the various components in modern text-independent speaker verification systems. We next describe the general hierarchy of speaker...

A multimodal person recognition architecture has been developed for the purpose of improving overall recognition performance and for addressing channel-specific performance shortfalls. This multimodal architecture includes the fusion of a face recognition system with the MIT/LLGMM/UBM speaker recognition architecture. This architecture exploits the complementary and redundant nature of the face...

One particularly difficult challenge for cross-channel speaker verification is the auxiliary microphone task introduced in the 2005 and 2006 NIST Speaker Recognition Evaluations, where training uses telephone speech and verification uses speech from multiple auxiliary microphones. This paper presents two approaches to compensate for the effects of auxiliary microphones on...

Many powerful methods for speaker recognition have been introduced in recent years--high-level features, novel classifiers, and channel compensation methods. A common arena for evaluating these methods has been the NIST speaker recognition evaluation (SRE). In the NIST SRE from 2002-2005, a popular approach was to fuse multiple systems based upon...

We present a novel framework for triaging (prioritizing and discarding) data to conserve resources for a speaker identification (SID) system. Our work is motivated by applications that require a SID system to process an overwhelming volume of audio data. We design a triage filter whose goal is to conserve recognizer...

This report explores auditory modeling as a basis for robust automatic speaker verification. Specifically, we have developed feature-extraction front-ends that incorporate (1) time-varying, level-dependent filtering, (2) variations in analysis filterbank size,and (3) nonlinear adaptation. Our methods are motivated both by a desire to better mimic auditory processing relative to traditional...

Audio diarization is the process of annotating an input audio channel with information that attributes (possibly overlapping) temporal regions of signal energy to their specific sources. These sources can include particular speakers, music, background noise sources, and other signal source/channel characteristics. Diarization can be used for helping speech recognition, facilitating...