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3. State-of-the-Art Speaker Recognition for Telephone and Video Speech: The JHU-MIT Submission for NIST SRE18

Author

David Snyder, Leibny Paola Garcia-Perera, Pedro A. Torres-Carrasquillo, Jonas Borgstrom, Sanjeev Khudanpur, Gregory Sell, Fred Richardson, Daniel Garcia-Romero, Nanxin Chen, Daniel Povey, Francois Grondin, Alan V. McCree, Najim Dehak, Réda Dehak, Jesús Villalba, and Suwon Shon

Subjects
Computer science, Speech recognition, NIST, State (computer science), Speaker recognition
Published
2019
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5. The MIT-LL, JHU and LRDE NIST 2016 Speaker Recognition Evaluation System

Author

Douglas E. Sturim, Youngjune Gwon, Swaroop Vattam, Réda Dehak, William M. Campbell, Harish Mallidi, Phani Sankar Nidadavolu, Fred Richardson, Shahan Nercessian, Pedro A. Torres-Carrasquillo, Ruizhi Li, and Najim Dehak

Subjects
030507 speech-language pathology & audiology, 03 medical and health sciences, Evaluation system, Computer science, Speech recognition, 0202 electrical engineering, electronic engineering, information engineering, NIST, 020206 networking & telecommunications, 02 engineering and technology, 0305 other medical science, Speaker recognition
Published
2017
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6. Corpora for the Evaluation of Robust Speaker Recognition Systems

Author

Douglas E. Sturim, Pedro A. Torres-Carrasquillo, and Joseph P. Campbell

Subjects
business.industry, Computer science, Speech recognition, 0202 electrical engineering, electronic engineering, information engineering, 020206 networking & telecommunications, 020201 artificial intelligence & image processing, 02 engineering and technology, Artificial intelligence, business, computer.software_genre, Speaker recognition, computer, Natural language processing
Published
2016
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7. The MITLL NIST LRE 2015 Language Recognition System

Author

Pedro A. Torres-Carrasquillo, Elliot Singer, Douglas A. Reynolds, Fred Richardson, Elizabeth C Godoy, Douglas E. Sturim, Najim Dehak, and Stephen Shum

Subjects
Task (computing), Computer science, business.industry, Speech recognition, Deep learning, Feature extraction, Pattern recognition (psychology), NIST, Context (language use), Artificial intelligence, Linear discriminant analysis, business, Natural language
Abstract

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.

Published
2016
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