  In speaker verification systems, the utilization of short utterances presents
a persistent challenge, leading to performance degradation primarily due to
insufficient phonetic information to characterize the speakers. To overcome
this obstacle, we propose a novel structure, MR-RawNet, designed to enhance the
robustness of speaker verification systems against variable duration utterances
using raw waveforms. The MR-RawNet extracts time-frequency representations from
raw waveforms via a multi-resolution feature extractor that optimally adjusts
both temporal and spectral resolutions simultaneously. Furthermore, we apply a
multi-resolution attention block that focuses on diverse and extensive temporal
contexts, ensuring robustness against changes in utterance length. The
experimental results, conducted on VoxCeleb1 dataset, demonstrate that the
MR-RawNet exhibits superior performance in handling utterances of variable
duration compared to other raw waveform-based systems.
