Voiceextender: Short-Utterance Text-Independent Speaker Verification With Guided Diffusion Model

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.