Self-supervised options are sometimes used instead of filter-bank options in speaker verification fashions. Nevertheless, these fashions had been initially designed to ingest filter-banks as inputs, and thus, coaching them on self-supervised options assumes that each function varieties require the identical quantity of studying for the duty. On this work, we observe that pre-trained self-supervised speech options inherently embrace data required for a downstream speaker verification job, and subsequently, we will simplify the downstream mannequin with out sacrificing efficiency. To this finish, we revisit the design of the downstream mannequin for speaker verification utilizing self-supervised options. We present that we will simplify the mannequin to make use of 97.51% fewer parameters whereas reaching a 29.93% common enchancment in efficiency on SUPERB. Consequently, we present that the simplified downstream mannequin is extra knowledge environment friendly in comparison with the baseline–it achieves higher efficiency with solely 60% of the coaching knowledge.
