  Speaker identification systems in a real-world scenario are tasked to
identify a speaker amongst a set of enrolled speakers given just a few samples
for each enrolled speaker. This paper demonstrates the effectiveness of
meta-learning and relation networks for this use case. We propose improved
relation networks for speaker verification and few-shot (unseen) speaker
identification. The use of relation networks facilitates joint training of the
frontend speaker encoder and the backend model. Inspired by the use of
prototypical networks in speaker verification and to increase the
discriminability of the speaker embeddings, we train the model to classify
samples in the current episode amongst all speakers present in the training
set. Furthermore, we propose a new training regime for faster model convergence
by extracting more information from a given meta-learning episode with
negligible extra computation. We evaluate the proposed techniques on VoxCeleb,
SITW and VCTK datasets on the tasks of speaker verification and unseen speaker
identification. The proposed approach outperforms the existing approaches
consistently on both tasks.
