  Speaker embeddings extracted with deep 2D convolutional neural networks are
typically modeled as projections of first and second order statistics of
channel-frequency pairs onto a linear layer, using either average or attentive
pooling along the time axis. In this paper we examine an alternative pooling
method, where pairwise correlations between channels for given frequencies are
used as statistics. The method is inspired by style-transfer methods in
computer vision, where the style of an image, modeled by the matrix of
channel-wise correlations, is transferred to another image, in order to produce
a new image having the style of the first and the content of the second. By
drawing analogies between image style and speaker characteristics, and between
image content and phonetic sequence, we explore the use of such channel-wise
correlations features to train a ResNet architecture in an end-to-end fashion.
Our experiments on VoxCeleb demonstrate the effectiveness of the proposed
pooling method in speaker recognition.
