  This paper describes the IDLab submission for the text-independent task of
the Short-duration Speaker Verification Challenge 2021 (SdSVC-21). This speaker
verification competition focuses on short duration test recordings and
cross-lingual trials, along with the constraint of limited availability of
in-domain DeepMine Farsi training data. Currently, both Time Delay Neural
Networks (TDNNs) and ResNets achieve state-of-the-art results in speaker
verification. These architectures are structurally very different and the
construction of hybrid networks looks a promising way forward. We introduce a
2D convolutional stem in a strong ECAPA-TDNN baseline to transfer some of the
strong characteristics of a ResNet based model to this hybrid CNN-TDNN
architecture. Similarly, we incorporate absolute frequency positional encodings
in an SE-ResNet34 architecture. These learnable feature map biases along the
frequency axis offer this architecture a straightforward way to exploit
frequency positional information. We also propose a frequency-wise variant of
Squeeze-Excitation (SE) which better preserves frequency-specific information
when rescaling the feature maps. Both modified architectures significantly
outperform their corresponding baseline on the SdSVC-21 evaluation data and the
original VoxCeleb1 test set. A four system fusion containing the two improved
architectures achieved a third place in the final SdSVC-21 Task 2 ranking.
