Short-Segment Speaker Verification with Pre-trained Models and Multi-Resolution Encoder

Abstract: Speaker verification (SV) utilizing features obtained from models pre-trained via self-supervised learning has recently demonstrated impressive performances. However, these pre-trained models (PTMs) usually have a temporal resolution of 20 ms, which is lower than typical filterbank features. It may be problematic especially for short-segment SV with an input segment shorter than 2 s, in which we need to extract as much information as possible from the input with a limited length. Although there have been approaches to utilize multi-resolution features from the HuBERT models, the window shifts were 320, 640, and 1600 samples when the sampling rate was 16 kHz and thus only lower resolution features were considered. In this study, we propose an SV system which utilizes PTM features along with filterbank features and those from the multi-resolution time domain encoder with window shifts of 25, 50, 100, and 200 samples. Experimental results on the VoxCeleb dataset with various input lengths showed consistent improvements over systems with various combinations of input features.