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Speaker verification (SV) performance deteriorates as utterances becomeshorter. To this end, we propose a new architecture called VoiceExtender whichprovides a promising solution for improving SV performance when handlingshort-duration speech signals. We use two guided diffusion models, the built-inand the external speaker embedding (SE) guided diffusion model, both of whichutilize a diffusion model-based sample generator that leverages SE guidance toaugment the speech features based on a short utterance. Extensive experimentalresults 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.

VoiceExtender: Short-utterance Text-independent Speaker Verification with Guided Diffusion Model