  Although few-shot learning has attracted much attention from the fields of
image and audio classification, few efforts have been made on few-shot speaker
identification. In the task of few-shot learning, overfitting is a tough
problem mainly due to the mismatch between training and testing conditions. In
this paper, we propose a few-shot speaker identification method which can
alleviate the overfitting problem. In the proposed method, the model of a
depthwise separable convolutional network with channel attention is trained
with a prototypical loss function. Experimental datasets are extracted from
three public speech corpora: Aishell-2, VoxCeleb1 and TORGO. Experimental
results show that the proposed method exceeds state-of-the-art methods for
few-shot speaker identification in terms of accuracy and F-score.
