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The MITLL NIST LRE 2015 Language Recognition System

June 21, 2016
  |
Conference Paper
Author:
Pedro A. Torres-Carrasquillo
…
Published in:
Odyssey 2016, 21-24 June 2016, pp. 196-203.
Topic:
language recognition
R&D area:
R&D group:

Summary

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, the evaluation included fixed training and open training tracks for the first time; second, language classification performance was measured across 6 language clusters using 20 language classes instead of an N-way language task; and third, performance was measured across a nominal 3-30 second range. Results are presented for the overall performance across the six language clusters for both the fixed and open training tasks. On the 6-cluster metric the Lincoln system achieved overall costs of 0.173 and 0.168 for the fixed and open tasks respectively.
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Summary

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...

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The MITLL NIST LRE 2015 Language Recognition System

Large-scale community detection on speaker content graphs

May 25, 2013
  |
Conference Paper
Author:
Stephen H. Shum
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 25-31 May 2013.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

We consider the use of community detection algorithms to perform speaker clustering on content graphs built from large audio corpora. We survey the application of agglomerative hierarchical clustering, modularity optimization methods, and spectral clustering as well as two random walk algorithms: Markov clustering and Infomap. Our results on graphs built from the NIST 2005+2006 and 2008+2010 Speaker Recognition Evaluations (SREs) provide insight into both the structure of the speakers present in the data and the intricacies of the clustering methods. In particular, we introduce an additional parameter to Infomap that improves its clustering performance on all graphs. Lastly, we also develop an automatic technique to purify the neighbors of each node by pruning away unnecessary edges.
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Summary

We consider the use of community detection algorithms to perform speaker clustering on content graphs built from large audio corpora. We survey the application of agglomerative hierarchical clustering, modularity optimization methods, and spectral clustering as well as two random walk algorithms: Markov clustering and Infomap. Our results on graphs built...

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Large-scale community detection on speaker content graphs

The MIT LL 2010 speaker recognition evaluation system: scalable language-independent speaker recognition

May 22, 2011
  |
Conference Paper
Author:
Douglas E. Sturim
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 22-27 May 2011, pp. 5272-5275.
Topic:
speaker recognition
R&D area:
R&D group:

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 present the MIT submission applied to the tasks of the 2010 NIST-SRE with two main goals--language-independent scalable modeling and robust nuisance mitigation. For modeling, exclusive use of inner product-based and cepstral systems produced a language-independent computationally-scalable system. For robustness, systems that captured spectral and prosodic information, modeled nuisance subspaces using multiple novel methods, and fused scores of multiple systems were implemented. The performance of the system is presented on a subset of the NIST SRE 2010 core tasks.
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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...

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The MIT LL 2010 speaker recognition evaluation system: scalable language-independent speaker recognition
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