2017
INTERSPEECH
INTERSPEECH 2017
The MIT-LL, JHU and LRDE NIST 2016 Speaker Recognition Evaluation System
Abstract
In this paper, the NIST 2016 SRE system that resulted from the collaboration between MIT Lincoln Laboratory and the team at Johns Hopkins University is presented. The submissions for the 2016 evaluation consisted of three fixed condition submissions and a single system open condition submission. The primary submission on the fixed (and core) condition resulted in an actual DCF of .618. Details of the submissions are discussed along with some discussion and observations of the 2016 evaluation campaign.
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Keyword Pioneer
— detection cost function
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio