  Despite achieving satisfactory performance in speaker verification using deep
neural networks, variable-duration utterances remain a challenge that threatens
the robustness of systems. To deal with this issue, we propose a speaker
verification system called RawNeXt that can handle input raw waveforms of
arbitrary length by employing the following two components: (1) A deep layer
aggregation strategy enhances speaker information by iteratively and
hierarchically aggregating features of various time scales and spectral
channels output from blocks. (2) An extended dynamic scaling policy flexibly
processes features according to the length of the utterance by selectively
merging the activations of different resolution branches in each block. Owing
to these two components, our proposed model can extract speaker embeddings rich
in time-spectral information and operate dynamically on length variations.
Experimental results on the VoxCeleb1 test set consisting of various duration
utterances demonstrate that RawNeXt achieves state-of-the-art performance
compared to the recently proposed systems. Our code and trained model weights
are available at https://github.com/wngh1187/RawNeXt.
