This paper describes the most relevant features of a collabora-tive multi-site submission to the NIST 2011 Language Recog-nition Evaluation (LRE), consisting of one primary and three contrastive systems, each fusing different combinations of 13 state-of-the-art (acoustic and phonotactic) language

Abstract � 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

tem for LRE 2007 evaluations. The primary system is a fusion of 4 acoustic systems and 9 phonotactic ones. The work builds on our previous LRE 2005 system [1] but also brings several new

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

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

and fusion parameters. Multiclass discrimina-tive fusion was performed separately for each nominal duration. A development set was defined, including the evaluation sets of LRE07 and LRE09 and the development data provided by NIST for 9 additional languages in the 2011 campaign. The official results, which

of the University of the Basque Country, with the key collaboration of Niko Brümmer, from Agnitio Research, South Africa, for defining the evaluation cri-terion and coding the script used to measure system performance. The evaluation

This paper presents the systems submitted by ATVS –

initiative able to collect and harmonise metadata about resources represents a valuable opportunity for the NLP community. In this paper we describe the LRE Map, reporting statistics on resources associated with LREC2012 papers and providing comparisons with LREC2010 data. The LRE Map, jointly launched

This study summarizes the overall solution and sub-systems developed by the Center for Robust Speech Systems (CRSS) at the University of Texas at Dallas to address the NIST LRE-2011 competition. CRSS-UTD employs five core sub-systems in the proposed