Publications
The MITLL NIST LRE 2011 language recognition system
Summary
Summary
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...
NAP for high level language identification
Summary
Summary
Varying channel conditions present a difficult problem for many speech technologies such as language identification (LID). Channel compensation techniques have been shown to significantly improve performance in LID for acoustic systems. For high-level token systems, nuisance attribute projection (NAP) has been shown to perform well in the context of speaker...
The MIT LL 2010 speaker recognition evaluation system: scalable language-independent speaker recognition
Summary
Summary
Research in the speaker recognition community has continued to address methods of mitigating variational nuisances. Telephone and auxiliary-microphone recorded speech emphasize the need for a robust way of dealing with unwanted variation. The design of recent 2010 NIST-SRE Speaker Recognition Evaluation (SRE) reflects this research emphasis. In this paper, we...
USSS-MITLL 2010 human assisted speaker recognition
Summary
Summary
The United States Secret Service (USSS) teamed with MIT Lincoln Laboratory (MIT/LL) in the US National Institute of Standards and Technology's 2010 Speaker Recognition Evaluation of Human Assisted Speaker Recognition (HASR). We describe our qualitative and automatic speaker comparison processes and our fusion of these processes, which are adapted from...
Transcript-dependent speaker recognition using mixer 1 and 2
Summary
Summary
Transcript-dependent speaker-recognition experiments are performed with the Mixer 1 and 2 read-transcription corpus using the Lincoln Laboratory speaker recognition system. Our analysis shows how widely speaker-recognition performance can vary on transcript-dependent data compared to conversational data of the same durations, given enrollment data from the same spontaneous conversational speech. A...
The MITLL NIST LRE 2009 language recognition system
Summary
Summary
This paper presents a description of the MIT Lincoln Laboratory language recognition system submitted to the NIST 2009 Language Recognition Evaluation (LRE). This system consists of a fusion of three core recognizers, two based on spectral similarity and one based on tokenization. The 2009 LRE differed from previous ones in...
The MIT Lincoln Laboratory 2008 speaker recognition system
Summary
Summary
In recent years methods for modeling and mitigating variational nuisances have been introduced and refined. A primary emphasis in this years NIST 2008 Speaker Recognition Evaluation (SRE) was to greatly expand the use of auxiliary microphones. This offered the additional channel variations which has been a historical challenge to speaker...
Discriminative N-gram selection for dialect recognition
Summary
Summary
Dialect recognition is a challenging and multifaceted problem. Distinguishing between dialects can rely upon many tiers of interpretation of speech data - e.g., prosodic, phonetic, spectral, and word. High-accuracy automatic methods for dialect recognition typically rely upon either phonetic or spectral characteristics of the input. A challenge with spectral system...
The MITLL NIST LRE 2007 language recognition system
Summary
Summary
This paper presents a description of the MIT Lincoln Laboratory language recognition system submitted to the NIST 2007 Language Recognition Evaluation. This system consists of a fusion of four core recognizers, two based on tokenization and two based on spectral similarity. Results for NIST?s 14-language detection task are presented for...
Beyond frame independence: parametric modelling of time duration in speaker and language recognition
Summary
Summary
In this work, we address the question of generating accurate likelihood estimates from multi-frame observations in speaker and language recognition. Using a simple theoretical model, we extend the basic assumption of independent frames to include two refinements: a local correlation model across neighboring frames, and a global uncertainty due to...