  In speaker verification, the extraction of voice representations is mainly
based on the Residual Neural Network (ResNet) architecture. ResNet is built
upon convolution layers which learn filters to capture local spatial patterns
along all the input, then generate feature maps that jointly encode the spatial
and channel information. Unfortunately, all feature maps in a convolution layer
are learnt independently (the convolution layer does not exploit the
dependencies between feature maps) and locally. This problem has first been
tackled in image processing. A channel attention mechanism, called
squeeze-and-excitation (SE), has recently been proposed in convolution layers
and applied to speaker verification. This mechanism re-weights the information
extracted across features maps. In this paper, we first propose an original
qualitative study about the influence and the role of the SE mechanism applied
to the speaker verification task at different stages of the ResNet, and then
evaluate several SE architectures. We finally propose to improve the SE
approach with a new pool- ing variant based on the concatenation of mean- and
standard- deviation-pooling. Results showed that applying SE only on the first
stages of the ResNet allows to better capture speaker information for the
verification task, and that significant discrimination gains on Voxceleb1-E,
Voxceleb1-H and SITW evaluation tasks have been noted using the proposed
pooling variant.
