  The recent advances in deep learning are mostly driven by availability of
large amount of training data. However, availability of such data is not always
possible for specific tasks such as speaker recognition where collection of
large amount of data is not possible in practical scenarios. Therefore, in this
paper, we propose to identify speakers by learning from only a few training
examples. To achieve this, we use a deep neural network with prototypical loss
where the input to the network is a spectrogram. For output, we project the
class feature vectors into a common embedding space, followed by
classification. Further, we show the effectiveness of capsule net in a few shot
learning setting. To this end, we utilize an auto-encoder to learn generalized
feature embeddings from class-specific embeddings obtained from capsule
network. We provide exhaustive experiments on publicly available datasets and
competitive baselines, demonstrating the superiority and generalization ability
of the proposed few shot learning pipelines.
