  Speaker identification systems are deployed in diverse environments, often
different from the lab conditions on which they are trained and tested. In this
paper, first, we show the problem of generalization using fixed thresholds
(computed using EER metric) for imposter identification in unseen speaker
recognition and then introduce a robust speaker-specific thresholding technique
for better performance. Secondly, inspired by the recent use of meta-learning
techniques in speaker verification, we propose an end-to-end meta-learning
framework for imposter detection which decouples the problem of imposter
detection from unseen speaker identification. Thus, unlike most prior works
that use some heuristics to detect imposters, the proposed network learns to
detect imposters by leveraging the utterances of the enrolled speakers.
Furthermore, we show the efficacy of the proposed techniques on VoxCeleb1, VCTK
and the FFSVC 2022 datasets, beating the baselines by up to 10%.
