4 research outputs found
I4U System Description for NIST SRE'20 CTS Challenge
This manuscript describes the I4U submission to the 2020 NIST Speaker
Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS)
Challenge. The I4U's submission was resulted from active collaboration among
researchers across eight research teams - IR (Singapore), UEF (Finland),
VALPT (Italy, Spain), NEC (Japan), THUEE (China), LIA (France), NUS
(Singapore), INRIA (France) and TJU (China). The submission was based on the
fusion of top performing sub-systems and sub-fusion systems contributed by
individual teams. Efforts have been spent on the use of common development and
validation sets, submission schedule and milestone, minimizing inconsistency in
trial list and score file format across sites.Comment: SRE 2021, NIST Speaker Recognition Evaluation Workshop, CTS Speaker
Recognition Challenge, 14-12 December 202
FAST SPEAKER RECOGNITION SCORING USING I-VECTOR POSTERIORS AND PROBABILISTIC LINEAR DISCRIMINANT ANALYSIS
A method for performing speaker recognition comprises: estimating respective uncertainties of acoustic coverage of respective speech utterance(s) by first and second speakers, the acoustic coverage representing respective sounds used by the speakers when speaking; representing the respective uncertainties of acoustic coverage in a manner that allows for efficient memory usage by discarding dependencies between uncertainties of different sounds for the speakers; representing the respective uncertainties of acoustic coverage in a manner that allows for efficient computation by representing an inverse of the respective uncertainties of acoustic coverage and then discarding the dependencies between the uncertainties of different sounds for the speakers; and computing a score between the speech utterance(s) by the speakers in a manner that leverages the respective uncertainties of the acoustic coverage during the comparison, the score being indicative of a likelihood that the speakers are the same speaker