  Speaker verification (SV) performance deteriorates as utterances become
shorter. To this end, we propose a new architecture called VoiceExtender which
provides a promising solution for improving SV performance when handling
short-duration speech signals. We use two guided diffusion models, the built-in
and the external speaker embedding (SE) guided diffusion model, both of which
utilize a diffusion model-based sample generator that leverages SE guidance to
augment the speech features based on a short utterance. Extensive experimental
results on the VoxCeleb1 dataset show that our method outperforms the baseline,
with relative improvements in equal error rate (EER) of 46.1%, 35.7%, 10.4%,
and 5.7% for the short utterance conditions of 0.5, 1.0, 1.5, and 2.0 seconds,
respectively.
