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The I4U Mega Fusion and Collaboration for NIST Speaker Recognition Evaluation 2016
Author(s) -
Kong Aik Lee
Publication year - 2017
Publication title -
interspeech 2022
Language(s) - English
Resource type - Conference proceedings
DOI - 10.21437/interspeech.2017-203
Subject(s) - nist , benchmarking , computer science , joint (building) , speaker recognition , scale (ratio) , speech recognition , paradigm shift , artificial intelligence , data science , engineering , architectural engineering , philosophy , physics , epistemology , marketing , quantum mechanics , business
The 2016 speaker recognition evaluation (SRE'16) is the latest edition in the series of benchmarking events conducted by the National Institute of Standards and Technology (NIST). I4U is a joint entry to SRE'16 as the result from the collaboration and active exchange of information among researchers from sixteen Institutes and Universities across 4 continents. The joint submission and several of its 32 subsystems were among top-performing systems. A lot of efforts have been devoted to two major challenges, namely, unlabeled training data and dataset shift from Switchboard-Mixer to the new Call My Net dataset. This paper summarizes the lessons learned, presents our shared view from the sixteen research groups on recent advances, major paradigm shift, and common tool chain used in speaker recognition as we have witnessed in SRE'16. More importantly, we look into the intriguing question of fusing a large ensemble of subsystems and the potential benefit of large-scale collaboration .

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